bluuebunny/crossref_metadata_2025 huggingface dataset:
Cluster Validation
Authors: Vilalta Ricardo , Stepinski Tomasz
Published: May 2011

Spacecrafts orbiting a selected suite of planets and moons of our solar system are continuously sending long sequences of data back to Earth. The availability of such data provides an opportunity to invoke tools from machine learning and pattern recognition to extract patterns that can help to understand geological processes shaping planetary surfaces. Due to the marked interest of the scientific community on this particular planet, we base our current discussion on Mars, where there are presently three spacecrafts in orbit (e.g., NASA’s Mars Odyssey Orbiter, Mars Reconnaissance Orbiter, ESA’s Mars Express). Despite the abundance of available data describing Martian surface, only a small fraction of the data is being analyzed in detail because current techniques for data analysis of planetary surfaces rely on a simple visual inspection and descriptive characterization of surface landforms (Wilhelms, 1990). The demand for automated analysis of Mars surface has prompted the use of machine learning and pattern recognition tools to generate geomorphic maps, which are thematic maps of landforms (or topographical expressions). Examples of landforms are craters, valley networks, hills, basins, etc. Machine learning can play a vital role in automating the process of geomorphic mapping. A learning system can be employed to either fully automate the process of discovering meaningful landform classes using clustering techniques; or it can be used instead to predict the class of unlabeled landforms (after an expert has manually labeled a representative sample of the landforms) using classification techniques. The impact of these techniques on the analysis of Mars topography can be of immense value due to the sheer size of the Martian surface that remains unmapped. While it is now clear that machine learning can greatly help in automating the detailed analysis of Mars’ surface (Stepinski et al., 2007; Stepinski et al., 2006; Bue and Stepinski, 2006; Stepinski and Vilalta, 2005), an interesting problem, however, arises when an automated data analysis has produced a novel classification of a specific site’s landforms. The problem lies on the interpretation of this new classification as compared to traditionally derived classifications generated through visual inspection by domain experts. Is the new classification novel in all senses? Is the new classification only partially novel, with many landforms matching existing classifications? This article discusses how to assess the value of clusters generated by machine learning tools as applied to the analysis of Mars’ surface.
Cost-Sensitive Learning
Authors: Sheng Victor S. , Ling Charles X.
Published: May 2011

Classification is the most important task in inductive learning and machine learning. A classifier can be trained from a set of training examples with class labels, and can be used to predict the class labels of new examples. The class label is usually discrete and finite. Many effective classification algorithms have been developed, such as naïve Bayes, decision trees, neural networks, and so on. However, most original classification algorithms pursue to minimize the error rate: the percentage of the incorrect prediction of class labels. They ignore the difference between types of misclassification errors. In particular, they implicitly assume that all misclassification errors cost equally. In many real-world applications, this assumption is not true. The differences between different misclassification errors can be quite large. For example, in medical diagnosis of a certain cancer, if the cancer is regarded as the positive class, and non-cancer (healthy) as negative, then missing a cancer (the patient is actually positive but is classified as negative; thus it is also called “false negative”) is much more serious (thus expensive) than the false-positive error. The patient could lose his/her life because of the delay in the correct diagnosis and treatment. Similarly, if carrying a bomb is positive, then it is much more expensive to miss a terrorist who carries a bomb to a flight than searching an innocent person.
Count Models for Software Quality Estimation
Authors: Gao Kehan , Khoshgoftaar Taghi M.
Published: May 2011

Timely and accurate prediction of the quality of software modules in the early stages of the software development life cycle is very important in the field of software reliability engineering. With such predictions, a software quality assurance team can assign the limited quality improvement resources to the needed areas and prevent problems from occurring during system operation. Software metrics-based quality estimation models are tools that can achieve such predictions. They are generally of two types: a classification model that predicts the class membership of modules into two or more quality-based classes (Khoshgoftaar et al., 2005b), and a quantitative prediction model that estimates the number of faults (or some other quality factor) that are likely to occur in software modules (Ohlsson et al., 1998). In recent years, a variety of techniques have been developed for software quality estimation (Briand et al., 2002; Khoshgoftaar et al., 2002; Ohlsson et al., 1998; Ping et al., 2002), most of which are suited for either prediction or classification, but not for both. For example, logistic regression (Khoshgoftaar & Allen, 1999) can only be used for classification, whereas multiple linear regression (Ohlsson et al., 1998) can only be used for prediction. Some software quality estimation techniques, such as case-based reasoning (Khoshgoftaar & Seliya, 2003), can be used to calibrate both prediction and classification models, however, they require distinct modeling approaches for both types of models. In contrast to such software quality estimation methods, count models such as the Poisson regression model (PRM) and the zero-inflated Poisson (ziP) regression model (Khoshgoftaar et al., 2001) can be applied to yield both with just one modeling approach. Moreover, count models are capable of providing the probability that a module has a given number of faults. Despite the attractiveness of calibrating software quality estimation models with count modeling techniques, we feel that their application in software reliability engineering has been very limited (Khoshgoftaar et al., 2001). This study can be used as a basis for assessing the usefulness of count models for predicting the number of faults and quality-based class of software modules.
Data Mining and the Text Categorization Framework
Authors: Cerchiello Paola
Published: May 2011

The aim of this contribution is to show one of the most important application of text mining. According to a wide part of the literature regarding the aforementioned field, great relevance is given to the classification task (Drucker et al., 1999, Nigam et al., 2000). The application contexts are several and multitask, from text filtering (Belkin & Croft, 1992) to word sense disambiguation (Gale et al., 1993) and author identification ( Elliot and Valenza, 1991), trough anti spam and recently also anti terrorism. As a consequence in the last decade the scientific community that is working on this task, has profuse a big effort in order to solve the different problems in the more efficient way. The pioneering studies on text categorization (TC, a.k.a. topic spotting) date back to 1961 (Maron) and are deeply rooted in the Information Retrieval context, so declaring the engineering origin of the field under discussion. Text categorization task can be briefly defined as the problem of assigning every single textual document into the relative class or category on the basis of the content and employing a classifier properly trained. In the following parts of this contribution we will formalize the classification problem detailing the main issues related.
Data Mining Applications in Steel Industry
Authors: Ordieres-Meré Joaquín , Castejón-Limas Manuel , González-Marcos Ana
Published: May 2011

The industrial plants, beyond subsisting, pursue to be leaders in increasingly competitive and dynamic markets. In this environment, quality management and technological innovation is less a choice than a must. Quality principles, such as those comprised in ISO 9000 standards, recommend companies to make their decisions with a based on facts approach; a policy much easily followed thanks to the all-pervasive introduction of computers and databases. With a view to improving the quality of their products, factory owners are becoming more and more interested in exploiting the benefits gained from better understanding their productive processes. Modern industries routinely measure the key variables that describe their productive processes while these are in operation, storing this raw information in databases for later analysis. Unfortunately, the distillation of useful information might prove problematic as the amount of stored data increases. Eventually, the use of specific tools capable of handling massive data sets becomes mandatory. These tools come from what it is known as ‘data mining’, a discipline that plays a remarkable role at processing and analyzing massive databases such as those found in the industry. One of the most interesting applications of data mining in the industrial field is system modeling. The fact that most frequently the relationships amongst process variables are nonlinear and the consequent difficulty to obtain explicit models to describe their behavior leads to data-based modeling as an improvement over simplified linear models.
Data Mining for Improving Manufacturing Processes
Authors: Rokach Lior
Published: May 2011

In many modern manufacturing plants, data that characterize the manufacturing process are electronically collected and stored in the organization’s databases. Thus, data mining tools can be used for automatically discovering interesting and useful patterns in the manufacturing processes. These patterns can be subsequently exploited to enhance the whole manufacturing process in such areas as defect prevention and detection, reducing flow-time, increasing safety, etc. When data mining is directed towards improving manufacturing process, there are certain distinctions that should be noted compared to the classical methods employed in quality engineering, such as the experimental design. In data mining the primary purpose of the targeted database is not data analysis; the volume of the collected data makes it impractical to explore it using standard statistical procedures (Braha and Shmilovici, 2003).
Data Mining for Structural Health Monitoring
Authors: Kanapady Ramdev , Lazarevic Aleksandar
Published: May 2011

Structural health monitoring denotes the ability to collect data about critical engineering structural elements using various sensors and to detect and interpret adverse “changes” in a structure in order to reduce life-cycle costs and improve reliability. The process of implementing and maintaining a structural health monitoring system consists of operational evaluation, data processing, damage detection and life prediction of structures. This process involves the observation of a structure over a period of time using continuous or periodic monitoring of spaced measurements, the extraction of features from these measurements, and the analysis of these features to determine the current state of health of the system. Such health monitoring systems are common for bridge structures and many examples are citied in (Maalej et al., 2002). The phenomenon of damage in structures includes localized softening or cracks in a certain neighborhood of a structural component due to high operational loads, or the presence of flaws due to manufacturing defects. Damage detection component of health monitoring system are useful for non-destructive evaluations that are typically employed in agile manufacturing systems for quality control and structures, such as turbine blades, suspension bridges, skyscrapers, aircraft structures, and various structures deployed in space for which structural integrity is of paramount concern (Figure 1). With the increasing demand for safety and reliability of aerospace, mechanical and civilian structures damage detection techniques become critical to reliable prediction of damage in these structural systems. Most currently used damage detection methods are manual such as tap test, visual or specially localized measurement techniques (Doherty, 1997). These techniques require that the location of the damage have to be on the surface of the structure. In addition, location of the damage has to be known a priori and these locations have to be readily accessible. This makes current maintenance procedure of large structural systems very time consuming and expensive due to its heavy reliance on human labor.
Data Mining Lessons Learned in the Federal Government
Authors: Pang Les
Published: May 2011

Data mining has been a successful approach for improving the level of business intelligence and knowledge management throughout an organization. This article identifies lessons learned from data mining projects within the federal government including military services. These lessons learned were derived from the following project experiences: • Defense Medical Logistics Support System Data Warehouse Program • Department of Defense (DoD) Defense Financial and Accounting Service (DFAS) “Operation Mongoose” • DoD Computerized Executive Information System (CEIS) • Department of Homeland Security’s Analysis, Dissemination, Visualization, Insight, and Semantic Enhancement (ADVISE) Program • Department of Transportation (DOT) Executive Reporting Framework System • Federal Aviation Administration (FAA) Aircraft Accident Data Mining Project • General Accountability Office (GAO) Data Mining of DoD Purchase and Travel Card Programs • U.S. Coast Guard Executive Information System • Veteran Administrations (VA) Demographics System
Data Pattern Tutor for AprioriAll and PrefixSpan
Authors: Alshalalfa Mohammed
Published: May 2011

Data mining can be described as data processing using sophisticated data search capabilities and statistical algorithms to discover patterns and correlations in large pre-existing databases (Agrawal & Srikant 1995; Zhao & Sourav 2003). From these patterns, new and important information can be obtained that will lead to the discovery of new meanings which can then be translated into enhancements in many current fields. In this paper, we focus on the usability of sequential data mining algorithms. Based on a conducted user study, many of these algorithms are difficult to comprehend. Our goal is to make an interface that acts as a “tutor” to help the users understand better how data mining works. We consider two of the algorithms more commonly used by our students for discovering sequential patterns, namely the AprioriAll and the PrefixSpan algorithms. We hope to generate some educational value, such that the tool could be used as a teaching aid for comprehending data mining algorithms. We concentrated our effort to develop the user interface to be easy to use by naïve end users with minimum computer literacy; the interface is intended to be used by beginners. This will help in having a wider audience and users for the developed tool.
Data Provenance
Authors: Sorathia Vikram
Published: May 2011

In recent years, our sensing capability has increased manifold. The developments in sensor technology, telecommunication, computer networking and distributed computing domain have created strong grounds for building sensor networks that are now reaching global scales (Balazinska et al., 2007). As data sources are increasing, the task of processing and analysis has gone beyond the capabilities of conventional desktop data processing tools. For quite a long time, data was assumed to be available on the single user-desktop; and handling, processing as well as analysis was carried out single-handedly. With proliferation of streaming data-sources and near real-time applications, it has become important to make provisions of automated identification and attribution of data-sets derived from such diverse sources. Considering the sharing and reuse of such diverse data-sets, the information about: the source of data, ownership, time-stamps, accuracy related details, processes and transformations subjected to it etc. have become essential. The piece of data that provide such information about the given data-set is known as Metadata. The need is recognized for creating and handling of metadata as an integrated part of large-scale systems. Considering the information requirements of scientific and research community, the efforts towards the building global data commons have came into existence (Onsrud & Campbell, 2007). A special type of service is required that can address the issues like: explication of licensing & Intellectual Property Rights, standards based automated generation of metadata, data provenance, archival and peer-review. While each of these terms is being addressed as individual research topics, the present article is focused only on Data Provenance.
Data Reduction with Rough Sets
Authors: Jensen Richard
Published: May 2011

Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. These techniques fall in to one of the following categories: those that transform the underlying meaning of the data features and those that are semantics-preserving. Feature selection (FS) methods belong to the latter category, where a smaller set of the original features is chosen based on a subset evaluation function. The process aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In knowledge discovery, feature selection methods are particularly desirable as they facilitate the interpretability of the resulting knowledge. For this, rough set theory has been successfully used as a tool that enables the discovery of data dependencies and the reduction of the number of features contained in a dataset using the data alone, while requiring no additional information.
Database Sampling for Data Mining
Authors: Lutu Patricia E.N.
Published: May 2011

In data mining, sampling may be used as a technique for reducing the amount of data presented to a data mining algorithm. Other strategies for data reduction include dimension reduction, data compression, and discretisation. For sampling, the aim is to draw, from a database, a random sample, which has the same characteristics as the original database. This chapter looks at the sampling methods that are traditionally available from the area of statistics, how these methods have been adapted to database sampling in general, and database sampling for data mining in particular.
Document Indexing Techniques for Text Mining
Authors: Ignacio Serrano José
Published: May 2011

Owing to the growing amount of digital information stored in natural language, systems that automatically process text are of crucial importance and extremely useful. There is currently a considerable amount of research work (Sebastiani, 2002; Crammer et al., 2003) using a large variety of machine learning algorithms and other Knowledge Discovery in Databases (KDD) methods that are applied to Text Categorization (automatically labeling of texts according to category), Information Retrieval (retrieval of texts similar to a given cue), Information Extraction (identification of pieces of text that contains certain meanings), and Question/Answering (automatic answering of user questions about a certain topic). The texts or documents used can be stored either in ad hoc databases or in the World Wide Web. Data mining in texts, the well-known Text Mining, is a case of KDD with some particular issues: on one hand, the features are obtained from the words contained in texts or are the words themselves. Therefore, text mining systems faces with a huge amount of attributes. On the other hand, the features are highly correlated to form meanings, so it is necessary to take the relationships among words into account, what implies the consideration of syntax and semantics as human beings do. KDD techniques require input texts to be represented as a set of attributes in order to deal with them. The text-to-representation process is called text or document indexing, and the attributes and called indexes. Accordingly, indexing is a crucial process in text mining because indexed representations must collect, only with a set of indexes, most of the information expressed in natural language in the texts with the minimum loss of semantics, in order to perform as well as possible.
Ethics of Data Mining
Authors: Cook Jack
Published: May 2011

Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions, even those that currently cannot be answered, is a characteristic of a good decision maker. Decision makers no longer have the luxury of making decisions based on gut feeling or intuition. Decisions must be supported by data; otherwise decision makers can expect to be questioned by stockholders, reporters, or attorneys in a court of law. Data mining can support and often direct decision makers in ways that are often counterintuitive. Although data mining can provide considerable insight, there is an “inherent risk that what might be inferred may be private or ethically sensitive” (Fule & Roddick, 2004, p. 159). Extensively used in telecommunications, financial services, insurance, customer relationship management (CRM), retail, and utilities, data mining more recently has been used by educators, government officials, intelligence agencies, and law enforcement. It helps alleviate data overload by extracting value from volume. However, data analysis is not data mining. Query-driven data analysis, perhaps guided by an idea or hypothesis, that tries to deduce a pattern, verify a hypothesis, or generalize information in order to predict future behavior is not data mining (Edelstein, 2003). It may be a first step, but it is not data mining. Data mining is the process of discovering and interpreting meaningful, previously hidden patterns in the data. It is not a set of descriptive statistics. Description is not prediction. Furthermore, the focus of data mining is on the process, not a particular technique, used to make reasonably accurate predictions. It is iterative in nature and generically can be decomposed into the following steps: (1) data acquisition through translating, cleansing, and transforming data from numerous sources, (2) goal setting or hypotheses construction, (3) data mining, and (4) validating or interpreting results. The process of generating rules through a mining operation becomes an ethical issue, when the results are used in decision-making processes that affect people or when mining customer data unwittingly compromises the privacy of those customers (Fule & Roddick, 2004). Data miners and decision makers must contemplate ethical issues before encountering one. Otherwise, they risk not identifying when a dilemma exists or making poor choices, since all aspects of the problem have not been identified.
Evolutionary Approach to Dimensionality Reduction
Authors: Saxena Amit , Kothari Megha , Pandey Navneet
Published: May 2011

Excess of data due to different voluminous storage and online devices has become a bottleneck to seek meaningful information therein and we are information wise rich but knowledge wise poor. One of the major problems in extracting knowledge from large databases is the size of dimension i.e. number of features, of databases. More often than not, it is observed that some features do not affect the performance of a classifier. There could be features that are derogatory in nature and degrade the performance of classifiers used subsequently for dimensionality reduction (DR). Thus one can have redundant features, bad features and highly correlated features. Removing such features not only improves the performance of the system but also makes the learning task much simpler. Data mining as a multidisciplinary joint effort from databases, machine learning, and statistics, is championing in turning mountains of data into nuggets (Mitra, Murthy, & Pal, 2002).
Evolutionary Data Mining for Genomics
Authors: Jourdan Laetitia
Published: May 2011

Knowledge discovery from genomic data has become an important research area for biologists. Nowadays, a lot of data is available on the web, but the corresponding knowledge is not necessarly also available. For example, the first draft of the human genome, which contains 3×109 letters, has been achieved in June 2000, but up to now only a small part of the hidden knowledge has been discovered. The aim of bioinformatics is to bring together biology, computer science, mathematics, statistics and information theory to analyze biological data for interpretation and prediction. Hence many problems encountered while studying genomic data may be modeled as data mining tasks, such as feature selection, classification, clustering, and association rule discovery. An important characteristic of genomic applications is the large amount of data to analyze and it is, most of the time, not possible to enumerate all the possibilities. Therefore, we propose to model these knowledge discovery tasks as combinatorial optimization tasks, in order to apply efficient optimization algorithms to extract knowledge from large datasets. To design an efficient optimization algorithm, several aspects have to be considered. The main one is the choice of the type of resolution method according to the characteristics of the problem. Is it an easy problem, for which a polynomial algorithm may be found? If yes, let us design such an algorithm. Unfortunately, most of the time the response to the question is ‘NO’ and only heuristics, that may find good but not necessarily optimal solutions, can be used. In our approach we focus on evolutionary computation, which has already shown an interesting ability to solve highly complex combinatorial problems. In this chapter, we will show the efficiency of such an approach while describing the main steps required to solve data mining problems from genomics with evolutionary algorithms. We will illustrate these steps with a real problem.
Flexible Mining of Association Rules
Authors: Shen Hong
Published: May 2011

The discovery of association rules showing conditions of data co-occurrence has attracted the most attention in data mining. An example of an association rule is the rule “the customer who bought bread and butter also bought milk,” expressed by T(bread; butter)? T(milk). Let I ={x1,x2,…,xm} be a set of (data) items, called the domain; let D be a collection of records (transactions), where each record, T, has a unique identifier and contains a subset of items in I. We define itemset to be a set of items drawn from I and denote an itemset containing k items to be k-itemset. The support of itemset X, denoted by Ã(X/D), is the ratio of the number of records (in D) containing X to the total number of records in D. An association rule is an implication rule ?Y, where X; ? I and X ?Y=0. The confidence of ? Y is the ratio of s(?Y/D) to s(X/D), indicating that the percentage of those containing X also contain Y. Based on the user-specified minimum support (minsup) and confidence (minconf), the following statements are true: An itemset X is frequent if s(X/D)> minsup, and an association rule ? XY is strong i ?XY is frequent and ( / ) ( / ) X Y D X Y ? ¸ minconf. The problem of mining association rules is to find all strong association rules, which can be divided into two subproblems: 1. Find all the frequent itemsets. 2. Generate all strong rules from all frequent itemsets. Because the second subproblem is relatively straightforward ? we can solve it by extracting every subset from an itemset and examining the ratio of its support; most of the previous studies (Agrawal, Imielinski, & Swami, 1993; Agrawal, Mannila, Srikant, Toivonen, & Verkamo, 1996; Park, Chen, & Yu, 1995; Savasere, Omiecinski, & Navathe, 1995) emphasized on developing efficient algorithms for the first subproblem. This article introduces two important techniques for association rule mining: (a) finding N most frequent itemsets and (b) mining multiple-level association rules.
Graphical Data Mining
Authors: Romanowski Carol J.
Published: May 2011

Data mining has grown to include many more data types than the “traditional” flat files with numeric or categorical attributes. Images, text, video, and the internet are now areas of burgeoning data mining research. Graphical data is also an area of interest, since data in many domains—such as engineering design, network intrusion detection, fraud detection, criminology, document analysis, pharmacology, and biochemistry—can be represented in this form. Graph mining algorithms and methods are fewer and less mature than those designed for numerical or categorical data. In addition, the distinction between graph matching and graph mining is not always clear. In graph mining, we often want to find all possible frequent subgraphs of all possible sizes that occur a specified minimum number of times. That goal involves iteratively matching incrementally larger subgraphs, while classical graph matching is a single search for a static subgraph. Also, graph mining is an unsupervised learning task. Instead of searching for a single match to a specific graph, we are looking for known or unknown graphs embedded in the data.
Hierarchical Document Clustering
Authors: Fung Benjamin C.M. , Wang Ke , Ester Martin
Published: May 2011

Document clustering is an automatic grouping of text documents into clusters so that documents within a cluster have high similarity in comparison to one another, but are dissimilar to documents in other clusters. Unlike document classification (Wang, Zhou, & He, 2001), no labeled documents are provided in clustering; hence, clustering is also known as unsupervised learning. Hierarchical document clustering organizes clusters into a tree or a hierarchy that facilitates browsing. The parent-child relationship among the nodes in the tree can be viewed as a topic-subtopic relationship in a subject hierarchy such as the Yahoo! directory. This chapter discusses several special challenges in hierarchical document clustering: high dimensionality, high volume of data, ease of browsing, and meaningful cluster labels. State-of-the-art document clustering algorithms are reviewed: the partitioning method (Steinbach, Karypis, & Kumar, 2000), agglomerative and divisive hierarchical clustering (Kaufman & Rousseeuw, 1990), and frequent itemset-based hierarchical clustering (Fung, Wang, & Ester, 2003). The last one, which was developed by the authors, is further elaborated since it has been specially designed to address the hierarchical document clustering problem.
Imprecise Data and the Data Mining Process
Authors: Brown Marvin L. , Kros John F.
Published: May 2011

Missing or inconsistent data has been a pervasive problem in data analysis since the origin of data collection. The management of missing data in organizations has recently been addressed as more firms implement large-scale enterprise resource planning systems (see Vosburg & Kumar, 2001; Xu et al., 2002). The issue of missing data becomes an even more pervasive dilemma in the knowledge discovery process, in that as more data is collected, the higher the likelihood of missing data becomes. The objective of this research is to discuss imprecise data and the data mining process. The article begins with a background analysis, including a brief review of both seminal and current literature. The main thrust of the chapter focuses on reasons for data inconsistency along with definitions of various types of missing data. Future trends followed by concluding remarks complete the chapter.
Incremental Learning
Authors: Bouchachia Abdelhamid
Published: May 2011

Data mining and knowledge discovery is about creating a comprehensible model of the data. Such a model may take different forms going from simple association rules to complex reasoning system. One of the fundamental aspects this model has to fulfill is adaptivity. This aspect aims at making the process of knowledge extraction continually maintainable and subject to future update as new data become available. We refer to this process as knowledge learning. Knowledge learning systems are traditionally built from data samples in an off-line one-shot experiment. Once the learning phase is exhausted, the learning system is no longer capable of learning further knowledge from new data nor is it able to update itself in the future. In this chapter, we consider the problem of incremental learning (IL). We show how, in contrast to off-line or batch learning, IL learns knowledge, be it symbolic (e.g., rules) or sub-symbolic (e.g., numerical values) from data that evolves over time. The basic idea motivating IL is that as new data points arrive, new knowledge elements may be created and existing ones may be modified allowing the knowledge base (respectively, the system) to evolve over time. Thus, the acquired knowledge becomes self-corrective in light of new evidence. This update is of paramount importance to ensure the adaptivity of the system. However, it should be meaningful (by capturing only interesting events brought by the arriving data) and sensitive (by safely ignoring unimportant events). Perceptually, IL is a fundamental problem of cognitive development. Indeed, the perceiver usually learns how to make sense of its sensory inputs in an incremental manner via a filtering procedure. In this chapter, we will outline the background of IL from different perspectives: machine learning and data mining before highlighting our IL research, the challenges, and the future trends of IL.
Integration of Data Mining and Operations Research
Authors: Meisel Stephan
Published: May 2011

Basically, Data Mining (DM) and Operations Research (OR) are two paradigms independent of each other. OR aims at optimal solutions of decision problems with respect to a given goal. DM is concerned with secondary analysis of large amounts of data (Hand et al., 2001). However, there are some commonalities. Both paradigms are application focused (Wu et al., 2003; White, 1991). Many Data Mining approaches are within traditional OR domains like logistics, manufacturing, health care or finance. Further, both DM and OR are multidisciplinary. Since its origins, OR has been relying on fields such as mathematics, statistics, economics and computer science. In DM, most of the current textbooks show a strong bias towards one of its founding disciplines, like database management, machine learning or statistics. Being multidisciplinary and application focused, it seems to be a natural step for both paradigms to gain synergies from integration. Thus, recently an increasing number of publications of successful approaches at the intersection of DM and OR can be observed. On the one hand, efficiency of the DM process is increased by use of advanced optimization models and methods originating from OR. On the other hand, effectiveness of decision making is increased by augmentation of traditional OR approaches with DM results. Meisel and Mattfeld (in press) provide a detailed discussion of the synergies of DM and OR.
Mass Informatics in Differential Proteomics
Authors: Zhang Xiang , Orcun Seza , Ouzzani Mourad , Oh Cheolhwan
Published: May 2011

Systems biology aims to understand biological systems on a comprehensive scale, such that the components that make up the whole are connected to one another and work in harmony. As a major component of systems biology, differential proteomics studies the differences between distinct but related proteomes such as normal versus diseased cells and diseased versus treated cells. High throughput mass spectrometry (MS) based analytical platforms are widely used in differential proteomics (Domon, 2006; Fenselau, 2007). As a common practice, the proteome is usually digested into peptides first. The peptide mixture is then separated using multidimensional liquid chromatography (MDLC) and is finally subjected to MS for further analysis. Thousands of mass spectra are generated in a single experiment. Discovering the significantly changed proteins from millions of peaks involves mass informatics. This paper introduces data mining steps used in mass informatics, and concludes with a descriptive examination of concepts, trends and challenges in this rapidly expanding field.
Microarray Data Mining
Authors: Fu Li-Min
Published: May 2011

Based on the concept of simultaneously studying the expression of a large number of genes, a DNA microarray is a chip on which numerous probes are placed for hybridization with a tissue sample. Biological complexity encoded by a deluge of microarray data is being translated into all sorts of computational, statistical or mathematical problems bearing on biological issues ranging from genetic control to signal transduction to metabolism. Microarray data mining is aimed to identify biologically significant genes and find patterns that reveal molecular network dynamics for reconstruction of genetic regulatory networks and pertinent metabolic pathways.
Mining 3D Shape Data for Morphometric Pattern Discovery
Authors: Shen Li , Makedon Fillia
Published: May 2011

Recent technological advances in 3D digitizing, noninvasive scanning, and interactive authoring have resulted in an explosive growth of 3D models in the digital world. There is a critical need to develop new 3D data mining techniques for facilitating the indexing, retrieval, clustering, comparison, and analysis of large collections of 3D models. These approaches will have important impacts in numerous applications including multimedia databases and mining, industrial design, biomedical imaging, bioinformatics, computer vision, and graphics. For example, in similarity search, new shape indexing schemes (e.g. (Funkhouser et al., 2003)) are studied for retrieving similar objects from databases of 3D models. These shape indices are designed to be quick to compute, concise to store, and easy to index, and so they are often relatively compact. In computer vision and medical imaging, more powerful shape descriptors are developed for morphometric pattern discovery (e.g., (Bookstein, 1997; Cootes, Taylor, Cooper, & Graham, 1995; Gerig, Styner, Jones, Weinberger, & Lieberman, 2001; Styner, Gerig, Lieberman, Jones, & Weinberger, 2003)) that aims to detect or localize shape changes between groups of 3D objects. This chapter describes a general shape-based 3D data mining framework for morphometric pattern discovery.
Multiclass Molecular Classification
Authors: Huey Ooi Chia
Published: May 2011

Molecular classification involves the classification of samples into groups of biological phenotypes. Studies on molecular classification generally focus on cancer for the following reason: Molecular classification of tumor samples from patients into different molecular types or subtypes is vital for diagnosis, prognosis, and effective treatment of cancer (Slonim, Tamayo, Mesirov, Golub, and Lander, 2000). Traditionally, such classification relies on observations regarding the location (Slonim et al., 2000) and microscopic appearance of the cancerous cells (Garber et al., 2001). These methods have proven to be slow and ineffective; there is no way of predicting with reliable accuracy the progress of the disease, since tumors of similar appearance have been known to take different paths in the course of time. With the advent of the microarray technology, data regarding the gene expression levels in each tumor sample may now prove to be a useful tool in molecular classification. This is because gene expression data provide snapshots of the activities within the cells and thus, the profile of the state of the cells in the tissue. The use of microarrays for gene expression profiling was first published in 1995 (Schena, Shalon, Davis, and Brown, 1995). In a typical microarray experiment, the expression levels of up to 10,000 or more genes are measured in each sample. The high-dimensionality of the data means that feature selection (FS) plays a crucial role in aiding the classification process by reducing the dimensionality of the input to the classification process. In the context of FS, the terms gene and feature will be used interchangeably in the context of gene expression data.
Pattern Preserving Clustering
Authors: Xiong Hui , Steinbach Michael , Tan Pang-Ning , Kumar Vipin , Zhou Wenjun
Published: May 2011

Clustering and association analysis are important techniques for analyzing data. Cluster analysis (Jain & Dubes, 1988) provides insight into the data by dividing objects into groups (clusters), such that objects in a cluster are more similar to each other than to objects in other clusters. Association analysis (Agrawal, Imielinski & Swami, 1993), on the other hand, provides insight into the data by finding a large number of strong patterns -- frequent itemsets and other patterns derived from them -- in the data set. Indeed, both clustering and association analysis are concerned with finding groups of strongly related objects, although at different levels. Association analysis finds strongly related objects on a local level, i.e., with respect to a subset of attributes, while cluster analysis finds strongly related objects on a global level, i.e., by using all of the attributes to compute similarity values. Recently, Xiong, Tan & Kumar (2003) have defined a new pattern for association analysis -- the hyperclique pattern -- which demonstrates a particularly strong connection between the overall similarity of all objects and the itemsets (local pattern) in which they are involved. The hyperclique pattern possesses a high affinity property: the objects in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another as measured by the cosine similarity (uncentered Pearson correlation coefficient). Since clustering depends on similarity, it seems reasonable that the hyperclique pattern should have some connection to clustering. Ironically, we found that hyperclique patterns are mostly destroyed by standard clustering techniques, i.e., standard clustering schemes do not preserve the hyperclique patterns, but rather, the objects comprising them are typically split among different clusters. To understand why this is not desirable, consider a set of hyperclique patterns for documents. The high affinity property of hyperclique patterns requires that these documents must be similar to one another; the stronger the hyperclique, the more similar the documents. Thus, for strong patterns, it would seem desirable (from a clustering viewpoint) that documents in the same pattern end up in the same cluster in many or most cases. As mentioned, however, this is not what happens for traditional clustering algorithms. This is not surprising since traditional clustering algorithms have no built in knowledge of these patterns and may often have goals that are in conflict with preserving patterns, e.g., minimize the distances of points from their closest cluster centroids.
Pattern Synthesis for Nonparametric Pattern Recognition
Authors: Viswanath P. , Murty Narasimha M. , Shalabh Bhatnagar
Published: May 2011

Parametric methods first choose the form of the model or hypotheses and estimates the necessary parameters from the given dataset. The form, which is chosen, based on experience or domain knowledge, often, need not be the same thing as that which actually exists (Duda, Hart & Stork, 2000). Further, apart from being highly error-prone, this type of methods shows very poor adaptability for dynamically changing datasets. On the other hand, non-parametric pattern recognition methods are attractive because they do not derive any model, but works with the given dataset directly. These methods are highly adaptive for dynamically changing datasets. Two widely used non-parametric pattern recognition methods are (a) the nearest neighbor based classification and (b) the Parzen-Window based density estimation (Duda, Hart & Stork, 2000). Two major problems in applying the non-parametric methods, especially, with large and high dimensional datasets are (a) the high computational requirements and (b) the curse of dimensionality (Duda, Hart & Stork, 2000). Algorithmic improvements, approximate methods can solve the first problem whereas feature selection (Isabelle Guyon & André Elisseeff, 2003), feature extraction (Terabe, Washio, Motoda, Katai & Sawaragi, 2002) and bootstrapping techniques (Efron, 1979; Hamamoto, Uchimura & Tomita, 1997) can tackle the second problem. We propose a novel and unified solution for these problems by deriving a compact and generalized abstraction of the data. By this term, we mean a compact representation of the given patterns from which one can retrieve not only the original patterns but also some artificial patterns. The compactness of the abstraction reduces the computational requirements, and its generalization reduces the curse of dimensionality effect. Pattern synthesis techniques accompanied with compact representations attempt to derive compact and generalized abstractions of the data. These techniques are applied with (a) the nearest neighbor classifier (NNC) which is a popular non-parametric classifier used in many fields including data mining since its conception in the early fifties (Dasarathy, 2002) and (b) the Parzen-Window based density estimation which is a well known non-parametric density estimation method (Duda, Hart & Stork, 2000).
A Philosophical Perspective on Knowledge Creation
Authors: Wickramasinghe Nilmini , Bali Rajeev K.
Published: May 2011

Today’s economy is increasingly based on knowledge and information (Davenport, & Grover 2001). Knowledge is now recognized as the driver of productivity and economic growth, leading to a new focus on the role of information, technology and learning in economic performance. Organizations trying to survive and prosper in such an economy are turning their focus to strategies, processes tools and technologies that can facilitate them to create knowledge. A vital and well respected technique in knowledge creation is data mining which enables critical knowledge to be gained from the analysis of large amounts of data and information. Traditional data mining and the KDD process (knowledge discovery in data bases) tends to view the knowledge product as a homogeneous product. Knowledge however, is a multifaceted construct, drawing upon various philosophical perspectives including Lockean/Leibnitzian and Hegelian/Kantian, exhibiting subjective and objective aspects as well as having tacit and explicit forms (Nonaka, 1994; Alavi & Leidner, 2001; Schultze & Leidner, 2002; Wickramasinghe et al, 2003). It is the thesis of this discussion that by taking a broader perspective of the resultant knowledge product from the KDD process; namely by incorporating both a people-based perspective and process-based perspective into the traditional KDD process, it will not only provide a more complete and macro perspective on knowledge creation but also a more balanced approach to knowledge creation which will in turn serve to enhance the extant knowledge base of an organization and facilitate the realization of superior decision making. The implications for data mining are clearly far reaching and are certain to help organizations more effectively realize the full potential of their knowledge assets, improve the likelihood of using/re-using the created knowledge and thereby enable them to be well positioned in today’s knowledge economy.