bluuebunny/crossref_metadata_2025 huggingface dataset:
Genetic Programming for Automatically Constructing Data Mining Algorithms
Authors: Freitas Alex A. , Pappa Gisele L.
Published: May 2011

At present there is a wide range of data mining algorithms available to researchers and practitioners (Witten & Frank, 2005; Tan et al., 2006). Despite the great diversity of these algorithms, virtually all of them share one feature: they have been manually designed. As a result, current data mining algorithms in general incorporate human biases and preconceptions in their designs. This article proposes an alternative approach to the design of data mining algorithms, namely the automatic creation of data mining algorithms by means of Genetic Programming (GP) (Pappa & Freitas, 2006). In essence, GP is a type of Evolutionary Algorithm – i.e., a search algorithm inspired by the Darwinian process of natural selection – that evolves computer programs or executable structures. This approach opens new avenues for research, providing the means to design novel data mining algorithms that are less limited by human biases and preconceptions, and so offer the potential to discover new kinds of patterns (or knowledge) to the user. It also offers an interesting opportunity for the automatic creation of data mining algorithms tailored to the data being mined.
Guide Manifold Alignment by Relative Comparisons
Authors: Xiong Liang
Published: May 2011

When we are faced with data, one common task is to learn the correspondence relationship between different data sets. More concretely, by learning data correspondence, samples that share similar intrinsic parameters, which are often hard to estimate directly, can be discovered. For example, given some face image data, an alignment algorithm is able to find images of two different persons with similar poses or expressions. We call this technique the alignment of data. Besides its usage in data analysis and visualization, this problem also has wide potential applications in various fields. For instance, in facial expression recognition, one may have a set of standard labeled images with known expressions, such as happiness, sadness, surprise, anger and fear, of a particular person. Then we can recognize the expressions of another person just by aligning his/her facial images to the standard image set. Its application can also be found directly in pose estimation. One can refer to (Ham, Lee & Saul, 2005) for more details. Although intuitive, without any premise this alignment problem can be very difficult. Usually, the samples are distributed in high-dimensional observation spaces, and the relation between features and samples’ intrinsic parameters can be too complex to be modeled explicitly. Therefore, some hypotheses about the data distribution are made. In the recent years, the manifold assumption of data distribution has been very popular in the field of data mining and machine learning. Researchers have realized that in many applications the samples of interest are actually confined to particular subspaces embedded in the high-dimensional feature space (Seung & Lee, 2000; Roweis & Saul, 2000). Intuitively, the manifold assumption means that certain groups of samples are lying in a non-linear low-dimensional subspace embedded in the observation space. This assumption has been verified to play an important role in human perception (Seung & Lee, 2000), and many effective algorithms are developed under it in the recent years. Under the manifold assumption, structural information of data can be utilized to facilitate the alignment.
Homeland Security Data Mining and Link Analysis
Authors: Thuraisingham Bhavani
Published: May 2011

Data mining is the process of posing queries to large quantities of data and extracting information often previously unknown using mathematical, statistical, and machine-learning techniques. Data mining has many applications in a number of areas, including marketing and sales, medicine, law, manufacturing, and, more recently, homeland security. Using data mining, one can uncover hidden dependencies between terrorist groups as well as possibly predict terrorist events based on past experience. One particular data-mining technique that is being investigated a great deal for homeland security is link analysis, where links are drawn between various nodes, possibly detecting some hidden links. This article provides an overview of the various developments in data-mining applications in homeland security. The organization of this article is as follows. First, we provide some background on data mining and the various threats. Then, we discuss the applications of data mining and link analysis for homeland security. Privacy considerations are discussed next as part of future trends. The article is then concluded.
Interest Pixel Mining
Authors: Li Qi , Ye Jieping , Kambhamettu Chandra
Published: May 2011

Visual media data such as an image is the raw data representation for many important applications, such as image retrieval (Mikolajczyk & Schmid 2001), video classification (Lin & Hauptmann, 2002), facial expression recognition (Wang & Ahuja 2003), face recognition (Zhao, Chellappa, Phillips & Rosenfeld 2003), etc. Reducing the dimensionality of raw visual media data is highly desirable since high dimensionality may severely degrade the effectiveness and the efficiency of retrieval algorithms. To obtain low-dimensional representation of visual media data, we can start by selecting good low-level features, such as colors, textures, and interest pixels (Swain & Ballard 1991; Gevers & Smeulders 1998; Schmid, Mohr & Bauckhage 2000). Pixels of an image may hold different interest strengths according to a specific filtering or convolution technique. The pixels of high interest strengths are expected to be more repeatable and stable than the pixels of low interest strengths across various imaging conditions, such as rotations, lighting conditions, and scaling. Interest pixel mining aims to detect a set of pixels that have the best repeatability across imaging conditions. (An algorithm for interest pixel mining is called a detector.) Interest pixel mining can be formulated into two steps: i) interest strength assignment via a specific filtering technique; and ii) candidate selection. The second step, candidate selection, plays an important role in preventing the output of interest pixels from being jammed in a small number of image regions in order to achieve best repeatability. Based on interest pixels, various image representations can be derived. A straightforward scheme is to represent an image as a collection of local appearances—the intensities of neighboring pixels—of interest pixels (Schmid & Mohr 1997). By ignoring the spatial relationship of interest pixels, this “unstructured” representation requires no image alignment, i.e., free from establishing pixel-to-pixel correspondence among imaging objects by image transformations such as rotation, translation, and scaling. Furthermore, the unstructured representation is very robust with respect to outlier regions in a retrieval application. However, the retrieval cost under unstructured representation is extremely expensive. In the context of face recognition, feature distribution is introduced to capture both global and local information of faces (Li, Ye & Kambhamettu 2006A). A limitation of feature distribution is the assumption of image alignment. A promising trend on interest pixel based representation is to build graph or tree representation for each image and measure the similarity of two images by the edit distance of their graphs or trees (Zhang & Shasha 1989). But as we will see in the later section, this trend is strongly supported by a recently proposed interest pixel mining method (Li, Ye & Kambhamettu 2008).
An Introduction to Kernel Methods
Authors: Camps-Valls Gustavo , Martínez-Ramón Manel , Rojo-Álvarez José Luis
Published: May 2011

Machine learning has experienced a great advance in the eighties and nineties due to the active research in artificial neural networks and adaptive systems. These tools have demonstrated good results in many real applications, since neither a priori knowledge about the distribution of the available data nor the relationships among the independent variables should be necessarily assumed. Overfitting due to reduced training data sets is controlled by means of a regularized functional which minimizes the complexity of the machine. Working with high dimensional input spaces is no longer a problem thanks to the use of kernel methods. Such methods also provide us with new ways to interpret the classification or estimation results. Kernel methods are emerging and innovative techniques that are based on first mapping the data from the original input feature space to a kernel feature space of higher dimensionality, and then solving a linear problem in that space. These methods allow us to geometrically design (and interpret) learning algorithms in the kernel space (which is nonlinearly related to the input space), thus combining statistics and geometry in an effective way. This theoretical elegance is also matched by their practical performance.
Learning Exceptions to Refine a Domain Expertise
Authors: Thomopoulos Rallou
Published: May 2011

This chapter deals with the problem of the cooperation of heterogeneous knowledge for the construction of a domain expertise, and more specifically for the discovery of new unexpected knowledge. Two kinds of knowledge are taken into account: • Expert statements. They constitute generic knowledge which rises from the experience of domain experts and describes commonly admitted mechanisms that govern the domain. This knowledge is represented as conceptual graph rules, which has the advantage to combine a logic-based formalism and an equivalent graphical representation, essential for non-specialist users (Bos, 1997). • Experimental data, given by international literature of the domain. They are represented in the relational model. These numerous data describe in detail, in a quantitative way, experiments that were carried out to deepen the knowledge of the domain, and the obtained results. These results may confirm the knowledge provided by the expert statements – or not. The cooperation of both kinds of knowledge aims, firstly, at testing the validity of the expert statements within the experimental data, secondly, at discovering refinements of the expert statements to consolidate the domain expertise. Two major differences between the two formalisms are the following. Firstly, the conceptual graphs represent knowledge at a more generic level than the relational data. Secondly, the conceptual graph model includes an ontological part (hierarchized vocabulary that constitutes the support of the model), contrary to the relational model. We introduce a process that allows one to test the validity of expert statements within the experimental data, that is, to achieve the querying of a relational database by a system expressed in the conceptual graph formalism. This process is based on the use of annotated conceptual graph patterns. When an expert statement appears not to be valid, a second-step objective is to refine it. This refinement consists of an automatic exception rule learning which provides unexpected knowledge in regard of previously established knowledge. The examples given in this chapter have been designed using the CoGui tool (http://www.lirmm. fr/cogui/) and concern a concrete application in the domain of food quality.
Legal and Technical Issues of Privacy Preservation in Data Mining
Authors: Wahlstrom Kirsten , Roddick John F. , Sarre Rick , Estivill-Castro Vladimir , de Vries Denise
Published: May 2011

To paraphrase Winograd (1992), we bring to our communities a tacit comprehension of right and wrong that makes social responsibility an intrinsic part of our culture. Our ethics are the moral principles we use to assert social responsibility and to perpetuate safe and just societies. Moreover, the introduction of new technologies can have a profound effect on our ethical principles. The emergence of very large databases, and the associated automated data analysis tools, present yet another set of ethical challenges to consider. Socio-ethical issues have been identified as pertinent to data mining and there is a growing concern regarding the (ab)use of sensitive information (Clarke, 1999; Clifton et al., 2002; Clifton and Estivill-Castro, 2002; Gehrke, 2002). Estivill-Castro et al., discuss surveys regarding public opinion on personal privacy that show a raised level of concern about the use of private information (Estivill-Castro et al., 1999). There is some justification for this concern; a 2001 survey in InfoWeek found that over 20% of companies store customer data with information about medical profile and/or customer demographics with salary and credit information, and over 15% store information about customers’ legal histories.
Locally Adaptive Techniques for Pattern Classification
Authors: Domeniconi Carlotta , Gunopulos Dimitrios
Published: May 2011

Pattern classification is a very general concept with numerous applications ranging from science, engineering, target marketing, medical diagnosis and electronic commerce to weather forecast based on satellite imagery. A typical application of pattern classification is mass mailing for marketing. For example, credit card companies often mail solicitations to consumers. Naturally, they would like to target those consumers who are most likely to respond. Often, demographic information is available for those who have responded previously to such solicitations, and this information may be used in order to target the most likely respondents. Another application is electronic commerce of the new economy. E-commerce provides a rich environment to advance the state-of-the-art in classification because it demands effective means for text classification in order to make rapid product and market recommendations. Recent developments in data mining have posed new challenges to pattern classification. Data mining is a knowledge discovery process whose aim is to discover unknown relationships and/or patterns from a large set of data, from which it is possible to predict future outcomes. As such, pattern classification becomes one of the key steps in an attempt to uncover the hidden knowledge within the data. The primary goal is usually predictive accuracy, with secondary goals being speed, ease of use, and interpretability of the resulting predictive model. While pattern classification has shown promise in many areas of practical significance, it faces difficult challenges posed by real world problems, of which the most pronounced is Bellman’s curse of dimensionality: it states the fact that the sample size required to perform accurate prediction on problems with high dimensionality is beyond feasibility. This is because in high dimensional spaces data become extremely sparse and are apart from each other. As a result, severe bias that affects any estimation process can be introduced in a high dimensional feature space with finite samples. Learning tasks with data represented as a collection of a very large number of features abound. For example, microarrays contain an overwhelming number of genes relative to the number of samples. The Internet is a vast repository of disparate information growing at an exponential rate. Efficient and effective document retrieval and classification systems are required to turn the ocean of bits around us into useful information, and eventually into knowledge. This is a challenging task, since a word level representation of documents easily leads 30000 or more dimensions. This chapter discusses classification techniques to mitigate the curse of dimensionality and reduce bias, by estimating feature relevance and selecting features accordingly. This issue has both theoretical and practical relevance, since many applications can benefit from improvement in prediction performance.
Meta-Learning
Authors: Giraud-Carrier Christophe , Brazdil Pavel , Soares Carlos , Vilalta Ricardo
Published: May 2011

The application of Machine Learning (ML) and Data Mining (DM) tools to classification and regression tasks has become a standard, not only in research but also in administrative agencies, commerce and industry (e.g., finance, medicine, engineering). Unfortunately, due in part to the number of available techniques and the overall complexity of the process, users facing a new data mining task must generally either resort to trialand- error or consultation of experts. Clearly, neither solution is completely satisfactory for the non-expert end-users who wish to access the technology more directly and cost-effectively. What is needed is an informed search process to reduce the amount of experimentation with different techniques while avoiding the pitfalls of local optima that may result from low quality models. Informed search requires meta-knowledge, that is, knowledge about the performance of those techniques. Metalearning provides a robust, automatic mechanism for building such meta-knowledge. One of the underlying goals of meta-learning is to understand the interaction between the mechanism of learning and the concrete contexts in which that mechanism is applicable. Metalearning differs from base-level learning in the scope of adaptation. Whereas learning at the base-level focuses on accumulating experience on a specific learning task (e.g., credit rating, medical diagnosis, mine-rock discrimination, fraud detection, etc.), learning at the meta-level is concerned with accumulating experience on the performance of multiple applications of a learning system. The meta-knowledge induced by meta-learning provides the means to inform decisions about the precise conditions under which a given algorithm, or sequence of algorithms, is better than others for a given task. While Data Mining software packages (e.g., SAS Enterprise Miner, SPSS Clementine, Insightful Miner, PolyAnalyst, KnowledgeStudio, Weka, Yale, Xelopes) provide user-friendly access to rich collections of algorithms, they generally offer no real decision support to non-expert end-users. Similarly, tools with emphasis on advanced visualization help users understand the data (e.g., to select adequate transformations) and the models (e.g., to tweak parameters, compare results, and focus on specific parts of the model), but treat algorithm selection as a post-processing activity driven by the users rather than the system. Data mining practitioners need systems that guide them by producing explicit advice automatically. This chapter shows how meta-learning can be leveraged to provide such advice in the context of algorithm selection.
Minimum Description Length Adaptive Bayesian Mining
Authors: Liberati Diego
Published: May 2011

In everyday life, it often turns out that one has to face a huge amount of data, often not completely homogeneous, often without an immediate grasp of an underlying simple structure. Many records, each instantiating many variables are usually collected with the help of several tools. Given the opportunity to have so many records on several possible variables, one of the typical goals one has in mind is to classify subjects on the basis of a hopefully reduced meaningful subset of the measured variables. The complexity of the problem makes it natural to resort to automatic classification procedures (Duda and Hart, 1973) (Hand et al., 2001). Then, a further questions could arise, like trying to infer a synthetic mathematical and/or logical model, able to capture the most important relations between the most influencing variables, while pruning (O’Connel 1974) the not relevant ones. Such interrelated aspects will be the focus of the present contribution. In the First Edition of this encyclopedia we already introduced three techniques dealing with such problems in a pair of articles (Liberati, 2005) (Liberati et al., 2005). Their rationale is briefly recalled in the following background section in order to introduce the kind of problems also faced by the different approach described in the present article, which will instead resort to the Adaptive Bayesian Networks implemented by Yarmus (2003) on a commercial wide spread data base tool like Oracle. Focus of the present article will thus be the use of Adaptive Bayesian Networks are in order to unsupervisedly learn a classifier direcly form data, whose minimal set of features is derived through the classical Minimun Description Lenght (Barron and Rissanen, 1998) popular in information theory. Reference will be again made to the same popular micro-arrays data set also used in (Liberati et al., 2005), not just to have a common benchmark useful to compare results and discuss complementary advantages of the various procedures, but also because of the increasing relevance of the bioinformatics field itself.
Mining Data Streams
Authors: Dasu Tamraparni , Weiss Gary
Published: May 2011

When a space shuttle takes off, tiny sensors measure thousands of data points every fraction of a second, pertaining to a variety of attributes like temperature, acceleration, pressure and velocity. A data gathering server at a networking company receives terabytes of data a day from various network elements like routers, reporting on traffic throughput, CPU usage, machine loads and performance. Each of these is an example of a data stream. Many applications of data streams arise naturally in industry (networking, e-commerce) and scientific fields (meteorology, rocketry). Data streams pose three unique challenges that make them interesting from a data mining perspective. 1. Size: The number of measurements as well as the number of attributes (variables) is very large. For instance, an IP network has thousands of elements each of which collects data every few seconds on multiple attributes like traffic, load, resource availability, topography, configuration and connections. 2. Rate of accumulation: The data arrives very rapidly, like “water from a fire hydrant”. Data storage and analysis techniques need to keep up with the data to avoid insurmountable backlogs. 3. Data transience: We get to see the raw data points at most once since the volumes of the raw data are too high to store or access.
Mining Data with Group Theoretical Means
Authors: Kern-Isberner Gabriele
Published: May 2011

Knowledge discovery refers to the process of extracting new, interesting, and useful knowledge from data and presenting it in an intelligible way to the user. Roughly, knowledge discovery can be considered a three-step process: preprocessing data; data mining, in which the actual exploratory work is done; and interpreting the results to the user. Here, I focus on the data-mining step, assuming that a suitable set of data has been chosen properly. The patterns that we search for in the data are plausible relationships, which agents may use to establish cognitive links for reasoning. Such plausible relationships can be expressed via association rules. Usually, the criteria to judge the relevance of such rules are either frequency based (Bayardo & Agrawal, 1999) or causality based (for Bayesian networks, see Spirtes, Glymour, & Scheines, 1993). Here, I will pursue a different approach that aims at extracting what can be regarded as structures of knowledge — relationships that may support the inductive reasoning of agents and whose relevance is founded on information theory. The method that I will sketch in this article takes numerical relationships found in data and interprets these relationships as structural ones, using mostly algebraic techniques to elaborate structural information.
Mining Repetitive Patterns in Multimedia Data
Authors: Yuan Junsong
Published: May 2011

One of the focused themes in data mining research is to discover frequent and repetitive patterns from the data. The success of frequent pattern mining (Han, Cheng, Xin, & Yan, 2007) in structured data (e.g., transaction data) and semi-structured data (e.g., text) has recently aroused our curiosity in applying them to multimedia data. Given a collection of unlabeled images, videos or audios, the objective of repetitive pattern discovery is to find (if there is any) similar patterns that appear repetitively in the whole dataset. Discovering such repetitive patterns in multimedia data brings in interesting new problems in data mining research. It also provides opportunities in solving traditional tasks in multimedia research, including visual similarity matching (Boiman & Irani, 2006), visual object retrieval (Sivic & Zisserman, 2004; Philbin, Chum, Isard, Sivic & Zisserman, 2007), categorization (Grauman & Darrell, 2006), recognition (Quack, Ferrari, Leibe & Gool, 2007; Amores, Sebe, & Radeva, 2007), as well as audio object search and indexing (Herley, 2006). • In image mining, frequent or repetitive patterns can be similar image texture regions, a specific visual object, or a category of objects. These repetitive patterns appear in a sub-collection of the images (Hong & Huang, 2004; Tan & Ngo, 2005; Yuan & Wu, 2007, Yuan, Wu & Yang, 2007; Yuan, Li, Fu, Wu & Huang, 2007). • In video mining, repetitive patterns can be repetitive short video clips (e.g. commercials) or temporal visual events that happen frequently in the given videos (Wang, Liu & Yang, 2005; Xie, Kennedy, Chang, Divakaran, Sun, & Lin, 2004; Yang, Xue, & Tian, 2005; Yuan, Wang, Meng, Wu & Li, 2007). • In audio mining, repetitive patterns can be repeated structures appearing in music (Lartillot, 2005) or broadcast audio (Herley, 2006). Repetitive pattern discovery is a challenging problem because we do not have any a prior knowledge of the possible repetitive patterns. For example, it is generally unknown in advance (i) what the repetitive patterns look like (e.g. shape and appearance of the repetitive object/contents of the repetitive clip); (ii) where (location) and how large (scale of the repetitive object or length of the repetitive clip) they are; (iii) how many repetitive patterns in total and how many instances each repetitive pattern has; or even (iv) whether such repetitive patterns exist at all. An exhaustive solution needs to search through all possible pattern sizes and locations, thus is extremely computationally demanding, if not impossible.
Mining Software Specifications
Authors: Lo David
Published: May 2011

Software is a ubiquitous component in our daily life. It ranges from large software systems like operating systems to small embedded systems like vending machines, both of which we frequently interact with. Reducing software related costs and ensuring correctness and dependability of software are certainly worthwhile goals to pursue. Due to the short-time-to-market requirement imposed on many software projects, documented software specifications are often lacking, incomplete and outdated (Deelstra, Sinnema & Bosch 2004). Lack of documented software specifications contributes to difficulties in understanding existing systems. The latter is termed program comprehension and is estimated to contribute up to 45% of total software cost which goes to billions of dollars (Erlikh 2000, Standish 1984; Canfora & Cimitile 2002; BEA 2007). Lack of specifications also hampers automated effort of program verification and testing (Ammons, Bodik & Larus 2002). One solution to address the above problems is mining (or automatic extraction of) software specification from program execution traces. Given a set of program traces, candidate partial specifications pertaining to the behavior a piece of software obeys can be mined. In this chapter, we will describe recent studies on mining software specifications. Software specification mining has been one of the new directions in data mining (Lo, Khoo & Liu 2007a, Lo & Khoo 2007). Existing specification mining techniques can be categorized based on the form of specifications they mine. We will categorize and describe specification mining algorithms for mining five different target formalisms: Boolean expressions, automata (Hopcroft, Motwani & Ullman 2001), Linear Temporal Logic (Huth & Ryan 2003), frequent patterns (Han & Kamber 2006) and Live Sequence Charts (Harel & Marelly 2003).
Modeling Score Distributions
Authors: Doloc-Mihu Anca
Published: May 2011

The goal of a web-based retrieval system is to find data items that meet a user’s request as fast and accurately as possible. Such a search engine finds items relevant to the user’s query by scoring and ranking each item in the database. Swets (1963) proposed to model the distributions of these scores to find an optimal threshold for separating relevant from non-relevant items. Since then, researchers suggested several different score distribution models, which offer elegant solutions to improve the effectiveness and efficiency of different components of search systems. Recent studies show that the method of modeling score distribution is beneficial to various applications, such as outlier detection algorithms (Gao & Tan, 2006), search engines (Manmatha, Feng, & Rath, 2001), information filtering (Zhang & Callan, 2001), distributed information retrieval (Baumgarten, 1999), video retrieval (Wilkins, Ferguson, & Smeaton, 2006), kernel type selection for image retrieval (Doloc-Mihu & Raghavan, 2006), and biometry (Ulery, Fellner, Hallinan, Hicklin, & Watson, 2006). The advantage of the score distribution method is that it uses the statistical properties of the scores, and not their values, and therefore, the obtained estimation may generalize better to not seen items than an estimation obtained by using the score values (Arampatzis, Beney, Koster, & van der Weide, 2000). In this chapter, we present the score distribution modeling approach, and then, we briefly survey theoretical and empirical studies on the distribution models, followed by several of its applications.
Multi-Group Data Classification via MILP
Authors: Yüksektepe Fadime Üney
Published: May 2011

Data classification is a supervised learning strategy that analyzes the organization and categorization of data in distinct classes. Generally, a training set, in which all objects are already associated with known class labels, is used in classification methods. The data classification algorithms work on this set by using input attributes and builds a model to classify new objects. In other words, the algorithm predicts output attribute values. Output attribute of the developed model is categorical (Roiger & Geatz, 2003). There are many applications of data classification in finance, health care, sports, engineering and science. Data classification is an important problem that has applications in a diverse set of areas ranging from finance to bioinformatics (Chen & Han & Yu, 1996; Edelstein, 2003; Jagota, 2000). Majority data classification methods are developed for classifying data into two groups. As multi-group data classification problems are very common but not widely studied, we focus on developing a new multi-group data classification approach based on mixed-integer linear programming.
Ontologies and Medical Terminologies
Authors: Geller James
Published: May 2011

The term “Ontology” was popularized in Computer Science by Thomas Gruber at the Stanford Knowledge Systems Lab (KSL). Gruber’s highly influential papers defined an ontology as “an explicit specification of a conceptualization.” (Gruber, 1992; Gruber 1993). Gruber cited a conceptualization as being “the objects and concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them.” (Genesereth & Nilsson, 1987). The term “Ontology” has been used in computer science at least since (Neches, 1991), but is derived from philosophy where it defines a “systematic account of existence,” usually contrasted with “Epistemology.” Gruber’s work is firmly grounded in Knowledge Representation and Artificial Intelligence research going back to McCarthy and Hayes classical paper (McCarthy & Hayes, 1969). Gruber’s work also builds on frame systems (Minsky, 1975; Fikes and Kehler, 1985) which have their roots in Semantic Networks, pioneered by (Quillian, 1968) and popularized through the successful and widespread KL-ONE family (Brachman & Schmolze, 1985). One can argue that Gruber’s ontologies are structurally very close to previous work in frame-based knowledge representation systems. However, Gruber focused on the notion of knowledge sharing which was a popular topic at KSL around the same time, especially in the form of the Knowledge Interchange Format (KIF) (Genesereth, 1991). Ontologies have recently moved center stage in Computer Science as they are a major ingredient of the Semantic Web (Berners-Lee et al., 2001), the next generation of the World-Wide Web. Ontologies have also been used in Data Mining (see below) and in (database) schema integration.
Preference Modeling and Mining for Personalization
Authors: Hwang Seung-won
Published: May 2011

As near-infinite amount of data are becoming accessible on the Web, it becomes more important to support intelligent personalized retrieval mechanisms, to help users identify the results of a manageable size satisfying user-specific needs. Example case studies include major search engines, such as Google and Yahoo, recently released personalized search, which adapts the ranking to the user-specific search context. Similarly, e-commerce sites, such as Amazon, are providing personalized product recommendation based on the purchase history and user browsing behaviors. To achieve this goal, it is important to model user preference and mine user preferences from user behaviors (e.g., click history) for personalization. In this article, we discuss recent efforts to extend mining research to preference and identify goals for the future works.
Projected Clustering for Biological Data Analysis
Authors: Deng Ping , Ma Qingkai , Wu Weili
Published: May 2011

Clustering can be considered as the most important unsupervised learning problem. It has been discussed thoroughly by both statistics and database communities due to its numerous applications in problems such as classification, machine learning, and data mining. A summary of clustering techniques can be found in (Berkhin, 2002). Most known clustering algorithms such as DBSCAN (Easter, Kriegel, Sander, & Xu, 1996) and CURE (Guha, Rastogi, & Shim, 1998) cluster data points based on full dimensions. When the dimensional space grows higher, the above algorithms lose their efficiency and accuracy because of the so-called “curse of dimensionality”. It is shown in (Beyer, Goldstein, Ramakrishnan, & Shaft, 1999) that computing the distance based on full dimensions is not meaningful in high dimensional space since the distance of a point to its nearest neighbor approaches the distance to its farthest neighbor as dimensionality increases. Actually, natural clusters might exist in subspaces. Data points in different clusters may be correlated with respect to different subsets of dimensions. In order to solve this problem, feature selection (Kohavi & Sommerfield, 1995) and dimension reduction (Raymer, Punch, Goodman, Kuhn, & Jain, 2000) have been proposed to find the closely correlated dimensions for all the data and the clusters in such dimensions. Although both methods reduce the dimensionality of the space before clustering, the case where clusters may exist in different subspaces of full dimensions is not handled well. Projected clustering has been proposed recently to effectively deal with high dimensionalities. Finding clusters and their relevant dimensions are the objectives of projected clustering algorithms. Instead of projecting the entire dataset on the same subspace, projected clustering focuses on finding specific projection for each cluster such that the similarity is reserved as much as possible.
Pseudo-Independent Models and Decision Theoretic Knowledge Discovery
Authors: Xiang Yang
Published: May 2011

Graphical models such as Bayesian networks (BNs) (Pearl, 1988; Jensen & Nielsen, 2007) and decomposable Markov networks (DMNs) (Xiang, Wong., & Cercone, 1997) have been widely applied to probabilistic reasoning in intelligent systems. Knowledge representation using such models for a simple problem domain is illustrated in Figure 1: Virus can damage computer files and so can a power glitch. Power glitch also causes a VCR to reset. Links and lack of them convey dependency and independency relations among these variables and the strength of each link is quantified by a probability distribution. The networks are useful for inferring whether the computer has virus after checking files and VCR. This chapter considers how to discover them from data. Discovery of graphical models (Neapolitan, 2004) by testing all alternatives is intractable. Hence, heuristic search are commonly applied (Cooper & Herskovits, 1992; Spirtes, Glymour, & Scheines, 1993; Lam & Bacchus, 1994; Heckerman, Geiger, & Chickering, 1995; Friedman, Geiger, & Goldszmidt, 1997; Xiang, Wong, & Cercone, 1997). All heuristics make simplifying assumptions about the unknown data-generating models. These assumptions preclude certain models to gain efficiency. Often assumptions and models they exclude are not explicitly stated. Users of such heuristics may suffer from such exclusion without even knowing. This chapter examines assumptions underlying common heuristics and their consequences to graphical model discovery. A decision theoretic strategy for choosing heuristics is introduced that can take into account a full range of consequences (including efficiency in discovery, efficiency in inference using the discovered model, and cost of inference with an incorrectly discovered model) and resolve the above issue.
Scientific Web Intelligence
Authors: Thelwall Mike
Published: May 2011

Scientific Web Intelligence (SWI) is a research field that combines techniques from data mining, web intelligence and scientometrics to extract useful information from the links and text of academic-related web pages, using various clustering, visualization and counting techniques. Its origins lie in previous scientometric research into mining offline academic data sources such as journal citation databases, in contrast to Web Science, which focuses on engineering an effective Web (Berners-Lee et al., 2006). Typical scientometric objectives are either evaluative: assessing the impact of research; or relational: identifying patterns of communication within and between research fields. From scientometrics, SWI also inherits a need to validate its methods and results so that the methods can be justified to end-users and the causes of the results can be found and explained.
Secure Building Blocks for Data Privacy
Authors: Han Shuguo
Published: May 2011

Rapid advances in automated data collection tools and data storage technology have led to the wide availability of huge amount of data. Data mining can extract useful and interesting rules or knowledge for decision making from large amount of data. In the modern world of business competition, collaboration between industries or companies is one form of alliance to maintain overall competitiveness. Two industries or companies may find that it is beneficial to collaborate in order to discover more useful and interesting patterns, rules or knowledge from their joint data collection, which they would not be able to derive otherwise. Due to privacy concerns, it is impossible for each party to share its own private data with one another if the data mining algorithms are not secure. Therefore, privacy-preserving data mining (PPDM) was proposed to resolve the data privacy concerns while yielding the utility of distributed data sets (Agrawal & Srikant, 2000; Lindell.Y. & Pinkas, 2000). Conventional PPDM makes use of Secure Multi-party Computation (Yao, 1986) or randomization techniques to allow the participating parties to preserve their data privacy during the mining process. It has been widely acknowledged that algorithms based on secure multi-party computation are able to achieve complete accuracy, albeit at the expense of efficiency.
Spectral Methods for Data Clustering
Authors: Li Wenyuan
Published: May 2011

With the rapid growth of the World Wide Web and the capacity of digital data storage, tremendous amount of data are generated daily from business and engineering to the Internet and science. The Internet, financial realtime data, hyperspectral imagery, and DNA microarrays are just a few of the common sources that feed torrential streams of data into scientific and business databases worldwide. Compared to statistical data sets with small size and low dimensionality, traditional clustering techniques are challenged by such unprecedented high volume, high dimensionality complex data. To meet these challenges, many new clustering algorithms have been proposed in the area of data mining (Han & Kambr, 2001). Spectral techniques have proven useful and effective in a variety of data mining and information retrieval applications where massive amount of real-life data is available (Deerwester et al., 1990; Kleinberg, 1998; Lawrence et al., 1999; Azar et al., 2001). In recent years, a class of promising and increasingly popular approaches — spectral methods — has been proposed in the context of clustering task (Shi & Malik, 2000; Kannan et al., 2000; Meila & Shi, 2001; Ng et al., 2001). Spectral methods have the following reasons to be an attractive approach to clustering problem: • Spectral approaches to the clustering problem offer the potential for dramatic improvements in efficiency and accuracy relative to traditional iterative or greedy algorithms. They do not intrinsically suffer from the problem of local optima. • Numerical methods for spectral computations are extremely mature and well understood, allowing clustering algorithms to benefit from a long history of implementation efficiencies in other fields (Golub & Loan, 1996). • Components in spectral methods have the naturally close relationship with graphs (Chung, 1997). This characteristic provides an intuitive and semantic understanding of elements in spectral methods. It Spectral Methods for Data Clustering Wenyuan Li Nanyang Technological University, Singapore Wee Keong Ng Nanyang Technological University, Singapore is important when the data is graph-based, such as links of WWW, or can be converted to graphs. In this paper, we systematically discuss applications of spectral methods to data clustering.
Statistical Web Object Extraction
Authors: Zhu Jun , Nie Zaiqing , Zhang Bo
Published: May 2011

The World Wide Web is a vast and rapidly growing repository of information. There are various kinds of objects, such as products, people, conferences, and so on, embedded in both statically and dynamically generated Web pages. Extracting the information about real-world objects is a key technique for Web mining systems. For example, the object-level search engines, such as Libra (http://libra.msra.cn) and Rexa (http://rexa.info), which help researchers find academic information like papers, conferences and researcher’s personal information, completely rely on structured Web object information. However, how to extract the object information from diverse Web pages is a challenging problem. Traditional methods are mainly template-dependent and thus not scalable to the huge number of Web pages. Furthermore, many methods are based on heuristic rules. So they are not robust enough. Recent developments in statistical machine learning make it possible to develop advanced statistical Web object extraction models. One key difference of Web object extraction from traditional information extraction from natural language text documents is that Web pages have plenty of structure information, such as two-dimensional spatial layouts and hierarchical vision tree representation. Statistical Web object extraction models can effectively leverage this information with properly designed statistical models. Another challenge of Web object extraction is that many text contents on Web pages are not regular natural language sentences. They have some structures but are lack of natural language grammars. Thus, existing natural language processing (NLP) techniques are not directly applicable. Fortunately, statistical Web object extraction models can easily merge with statistical NLP methods which have been the theme in the field of natural language processing during the last decades. Thus, the structure information on Web pages can be leveraged to help process text contents, and traditional NLP methods can be used to extract more features. Finally, the Web object extraction from diverse and large-scale Web pages provides a valuable and challenging problem for machine learning researchers. To nicely solve the problem, new learning methodology and new models (Zhu et al., 2007b) have to be developed.
Survival Data Mining
Authors: Chen Qiyang
Published: May 2011

Survival analysis (SA) consists of a variety of methods for analyzing the timing of events and/or the times of transition among several states or conditions. The event of interest can only happen at most once to any individual or subject. Alternate terms to identify this process include Failure Analysis (FA), Reliability Analysis (RA), Lifetime Data Analysis (LDA), Time to Event Analysis (TEA), Event History Analysis (EHA), and Time Failure Analysis (TFA) depending on the type of application the method is used for (Elashoff, 1997). Survival Data Mining (SDM) is a new term being coined recently (SAS, 2004). There are many models and variations on the different models for SA or failure analysis. This chapter discusses some of the more common methods of SA with real life applications. The calculations for the various models of SA are very complex. Currently, there are multiple software packages to assist in performing the necessary analyses much more quickly.
Transferable Belief Model
Authors: Smets Philippe
Published: May 2011

This note is a very short presentation of the transferable belief model (TBM), a model for the representation of quantified beliefs based on belief functions. Details must be found in the recent literature. The TBM covers the same domain as the subjective probabilities except probability functions are replaced by belief functions which are much more general. The model is much more flexible than the Bayesian one and allows the representation of states of beliefs not adequately represented with probability functions. The theory of belief functions is often called the Dempster-Shafer’s theory, but this term is unfortunately confusing.
Using Dempster-Shafer Theory in Data Mining
Authors: Beynon Malcolm J.
Published: May 2011

The origins of Dempster-Shafer theory (DST) go back to the work by Dempster (1967) who developed a system of upper and lower probabilities. Following this, his student Shafer (1976), in their book “A Mathematical Theory of Evidence” developed Dempster’s work, including a more thorough explanation of belief functions, a more general term for DST. In summary, it is a methodology for evidential reasoning, manipulating uncertainty and capable of representing partial knowledge (Haenni & Lehmann, 2002; Kulasekere, Premaratne, Dewasurendra, Shyu, & Bauer, 2004; Scotney & McClean, 2003). The perception of DST as a generalisation of Bayesian theory (Shafer & Pearl, 1990), identifies its subjective view, simply, the probability of an event indicates the degree to which someone believes it. This is in contrast to the alternative frequentist view, understood through the “Principle of I sufficient reasoning”, whereby in a situation of ignorance a Bayesian approach is forced to evenly allocate subjective (additive) probabilities over the frame of discernment. See Cobb and Shenoy (2003) for a contemporary comparison between Bayesian and belief function reasoning. The development of DST includes analogies to rough set theory (Wu, Leung, & Zhang, 2002) and its operation within neural and fuzzy environments (Binaghi, Gallo, & Madella, 2000; Yang, Chen, & Wu, 2003). Techniques based around belief decision trees (Elouedi, Mellouli, & Smets, 2001), multi-criteria decision making (Beynon, 2002) and non-paramnteric regression (Petit-Renaud & Denoeux, 2004), utilise DST to allow analysis in the presence of uncertainty and imprecision. This is demonstrated, in this article, with the ‘Classification and Ranking belief Simplex’ (CaRBS) technique for object classification, see Beynon (2005a).