OpenRaiser/Intern-Atlas huggingface dataset:
The generalized Bayesian committee machine
Authors: Volker Tresp
Venue: KDD
Year: 2000

Article Free Access Share on The generalized Bayesian committee machine Author: Volker Tresp Siemens AG, Corporate Technology, Otto-Hahn-Ring 6, 81730 München, Germany Siemens AG, Corporate Technology, Otto-Hahn-Ring 6, 81730 München, GermanyView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 130–139https://doi.org/10.1145/347090.347118Published:01 August 2000Publication History 11citation406DownloadsMetricsTotal Citations11Total Downloads406Last 12 Months7Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Application of neural networks to biological data mining: a case study in protein sequence classification
Authors: Jason Tsong-Li Wang, Qicheng Ma, Dennis E. Shasha, Cathy H. Wu
Venue: KDD
Year: 2000

Biological data mining aims to extract signi cant information from DNA, RNA and proteins. The signi cant information may refer to motifs, functional sites, clustering and classi cation rules. This paper presents an example of biological data mining: the classi cation of protein sequences using neural netw orks. We proposenew tec hniques to extract features from protein data and use them in combination with the Ba yesianneural network to classify protein sequences obtained from the PIR protein database maintained at the National Biomedical Research F oundation. T o evaluate the performance of the proposed approach, we c o mpare it with other protein classi ers built based on sequence alignment and machine learning methods. Experimental results sho w the high precision of the proposed classi er and the complementarity of the tools studied in the paper.
Efficient mining of weighted association rules (WAR)
Authors: Wei Wang, Jiong Yang, Philip S. Yu
Venue: KDD
Year: 2000

In this paper, we extend the tradition association rule problem by allowing a weight to be associated with each item in a transaction, to re ect interest intensity of the item within the transaction. This provides us in turn with an opportunity to associate a weight parameter with each item in the resulting association rule. We call it weighted association rule WAR. WAR not only improves the con dence of the rules, but also provides a mechanism to do more effective target marketing by identifying or segmenting customers based on their potential degree of loyalty o r v olume of purchases. Our approach mines WARs by rst ignoring the weight and nding the frequent itemsets via a traditional frequent itemset discovery algorithm, and is followed by i n troducing the weight during the rule generation. It is shown by experimental results that our approach not only results in shorter average execution times, but also produces higher quality results than the generalization of previous known methods on quantitative association rules.
Growing decision trees on support-less association rules
Authors: Ke Wang, Senqiang Zhou, Yu He
Venue: KDD
Year: 2000

this paper, we answer these questions and propose a general method for turning an arbitrary set of rules, in particular, association rules, into a classifier. In the past few years, many association rules and mining algorithms were proposed. However, the user often faces difficulties in making sense out of association rules. Indeed, no indication is given by association rules as to whether a specific but more confident rule or a general but less confident rule should be used to recommend products to new customers, and what hit rate a set of association rules will result in. Knowing such information is extremely important in a business decision making. Addressing such issues is the topic of this paper.
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Authors: Kenji Yamanishi, Jun&apos, ichi Takeuchi, Graham J. Williams, Peter Milne
Venue: KDD
Year: 2000

Outlier detection is a fundamental issue in data mining, speci cally in fraud detection, network intrusion detection, network monitoring, etc. SmartSifter, which we abbreviate as SS, is an outlier detection engine adrressing this problem from the viewpoint of statistical learning theory. This paper provides a theoretical basis for SS and empirically demonstrates its effectiveness. SS detects outliers in an online process through the on-line unsupervised learning of a probabilistic model (using a finite mixture model) of the information source. Each time a datum is input SS employs an on-line discounting learning algorithm to learn the probabilistic model. A score is given to the datum based on the learned model, with a high score indicating a high possibility of being a statistical outlier. The novel features of SS are: 1) it is adaptive to non-stationary sources of data; 2) a score has a clear statistical/information-theoretic meaning; 3) it is computationally inexpensive; and 4) it can handle both categorical and continuous variables. An experimental application to network intrusion detection shows that SS was able to identify data with high scores that corresponded to attacks, with low computational costs. Further experimental application has identified a number of meaningful rare cases in actual health insurance pathology data from Australia&apos;s Health Insurance Commission.
Mining asynchronous periodic patterns in time series data
Authors: Jiong Yang, Wei Wang, Philip S. Yu
Venue: KDD
Year: 2000

Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min/spl I.bar/rep and max/spl I.bar/dis are employed to specify the minimum number of repetitions that is required within each segment of nondisrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two-phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid subsequence. We also show that this algorithm cannot only provide linear time complexity with respect to the length of the sequence but also achieve space efficiency.
A classifier for semi-structured documents
Authors: Jeonghee Yi, Neel Sundaresan
Venue: KDD
Year: 2000

Article Free Access Share on A classifier for semi-structured documents Authors: Jeonghee Yi Computer Science, UCLA, 405 Hilgard Av., LA, CA Computer Science, UCLA, 405 Hilgard Av., LA, CAView Profile , Neel Sundaresan NehaNet Corp., San Jose, CA NehaNet Corp., San Jose, CAView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000Pages 340–344https://doi.org/10.1145/347090.347164Published:01 August 2000Publication History 65citation1,129DownloadsMetricsTotal Citations65Total Downloads1,129Last 12 Months51Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Authors: Xiuzhen Zhang, Guozhu Dong, Kotagiri Ramamohanarao
Venue: KDD
Year: 2000

Emerging patterns (EPs) were proposed recently to capture changes or differences between datasets: an EP is a multivariate feature whose support increases sharply from a background dataset to a target dataset, and the support ratio is called its growth rate. Interesting long EPs often have low support; mining such EPs from high-dimensional datasets is a great challenge due to the combinatorial explosion of the number of candidates. We propose a Constraint-based EP Miner, ConsEPMiner, that utilizes two types of constraints for effectively pruning the search space: External constraints are user-given minimums on support, growth rate, and growth-rate improvement to con ne the resulting EP set. Inherent constraints -- same subset support, top growth rate, and same origin -- are derived from the properties of EPs and datasets, and are solely for pruning the search space and saving computation. ConsEPMiner can efficiently mine all EPs at low support on large high-dimensional datasets, with low minim...
Data mining case study: modeling the behavior of offenders who commit serious sexual assaults
Authors: Richard Adderley, Peter B. Musgrove
Venue: KDD
Year: 2001

This paper looks at the use of a Self Organizing Map (SOM), to link of records of crimes of serious sexual attacks. Once linked a profile can be derived of the offender(s) responsible.The data was drawn from the major crimes database at the National Crime Faculty of the National Police Staff College Bramshill UK. The data was encoded from text by a small team of specialists working to a well-defined protocol. The encoded data was analyzed using SOMs. Two exercises were conducted. These resulted in the linking of several offences in to clusters each of which were sufficiently similar to have possibly been committed by the same offender(s). A number of clusters were used to form profiles of offenders. Some of these profiles were confirmed by independent analysts as either belonging to known offenders or appeared sufficiently interesting to warrant further investigation.The prototype was developed over 10 weeks. This contrasts with an in-house study using a conventional approach, which took 2 years to reach similar results. As a consequence of this study the NCF intends to pursue an in-depth follow up study.
Mining massively incomplete data sets by conceptual reconstruction
Authors: Charu C. Aggarwal, Srinivasan Parthasarathy
Venue: KDD
Year: 2001

Incomplete data sets have become almost ubiquitous in a wide variety of application domains. Common examples can be found in climate and image data sets, sensor data sets and medical data sets. The incompleteness in these data sets may arise from a number of factors: in some cases it may simply be a reflection of certain measurements not being available at the time; in others the information may be lost due to partial system failure; or it may simply be a result of users being unwilling to specify attributes due to privacy concerns. When a significant fraction of the entries are missing in all of the attributes, it becomes very difficult to perform any kind of reasonable extrapolation on the original data. For such cases, we introduce the novel idea of conceptual reconstruction, in which we create effective conceptual representations on which the data mining algorithms can be directly applied. The attraction behind the idea of conceptual reconstruction is to use the correlation structure of the data in order to express it in terms of concepts rather the original dimensions. As a result, the reconstruction procedure estimates only those conceptual aspects of the data which can be mined from the incomplete data set, rather than force errors created by extrapolation. We demonstrate the effectiveness of the approach on a variety of real data sets.
Applications of generalized support vector machines to predictive modeling
Authors: Nitin Agrarwal
Venue: KDD
Year: 2001

The work of the Russian mathematician Vladimir Vapnik (AT&T Labs) enables us to go back to the roots of theoretical statistics, leaving behind Fisher's parameters in favor of the general approaches started in the 1930s by Glivenko-Cantelli-Kolmogorov. Nowadays, it has become possible to model millions of events described by thousands of variables, within a reasonable time for a specific application. The SRM approach works with a family of models and calibrates the family of models to a point which is the best compromise between accuracy and robustness. It also measures the complexity of the model using VC dimension which is not plagued by number of parameters. Hence models for large events described by several parameters can be generalized. This opens up great prospects in numerous fields like Customer Relationship Management, Network Optimization, Risk Management, Manufacturing Yield Management, and a number of other data-rich problems.
Challenges for knowledge discovery in biology
Authors: Russ B. Altman
Venue: KDD
Year: 2001

Bioinformatics is the study of information flow in biology. Interest in the field has exploded in the last 10 years with the emergence of techniques for large scale experimental data collection-including genome sequencing, gene expression analysis, protein interaction detection, high-throughput structure determination and others. These techniques, in the context of a large online published literature, have created relatively large data sets (at least by biological standards) that are not possible to analyze manually. There is therefore a critical need for methods to analyze these data and reduce them to new knowledge. The principle challenges to the field include the great diversity of data types and questions that are asked of the data, and the communication difficulties that can exist between experts in biology and experts in machine learning. In this talk, I will provide an introduction to the major biological questions that are being addressed, why they are important, and how the field is trying to address them with technical approaches.
Segmentation-based modeling for advanced targeted marketing
Authors: Chidanand Apté, Eric Bibelnieks, Ramesh Natarajan, Edwin P. D. Pednault, Fateh Tipu, Deb Campbell, Bryan Nelson
Venue: KDD
Year: 2001

Fingerhut Business Intelligence (BI) has a long and successful history of building statistical models to predict consumer behavior. The models constructed are typically segmentation-based models in which the target audience is split into subpopulations (i.e., customer segments) and individually tailored statistical models are then developed for each segment. Such models are commonly employed in the direct-mail industry; however, segmentation is often performed on an ad-hoc basis without directly considering how segmentation affects the accuracy of the resulting segment models. Fingerhut BI approached IBM Research with the problem of how to build segmentation-based models more effectively so as to maximize predictive accuracy. The IBM Advanced Targeted Marketing-Single EventsTM (IBM ATM-SETM) solution is the result of IBM Research and Fingerhut BI directing their efforts jointly towards solving this problem. This paper presents an evaluation of ATM-SE's modeling capabilities using data from Fingerhut's catalog mailings.
Evaluating the novelty of text-mined rules using lexical knowledge
Authors: Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, Joydeep Ghosh
Venue: KDD
Year: 2001

A data-mining system may discover a large body of rules; however, relatively few of these may convey useful new knowledge to the user. Several metrics for evaluating the &quot;interestingness&quot; of mined rules have been proposed. However, most of these measure simplicity (e.g. rule size), certainty (e.g. confidence), or utility (e.g. support). Another important aspect of interestingness is novelty: does the rule represent an association that is currently unknown. In this paper, we present a new method of estimating the novelty of rules discovered by data-mining methods using WordNet, a lexical knowledge-base of English words. We have shown that novelty of a rule can be assessed by the average semantic distance in a knowledge hierarchy between the words in the antecedent and the consequent of the rule - the more the average distance, more is the novelty of the rule. We present an experimental evaluation of this novelty metric by applying it to rules mined from book descriptions extracted from A...
Interactive path analysis of web site traffic
Authors: Pavel Berkhin, Jonathan D. Becher, Dee Jay Randall
Venue: KDD
Year: 2001

The goal of Path Analysis is to understand visitors' navigation of a Web site. The fundamental analysis component is a path. A path is a finite sequence of elements, typically representing URLs or groups of URLs. A full path is an abstraction of a visit or a session, which can contain attributes described below. Subpaths represent interesting subsequences of the full paths.Path Analysis provides user-configurable extraction, filtering, preprocessing, noise reduction, descriptive statistics and detailed analysis of three basic specific objects: elements, (sub)paths, and couples of elements. In each case, lists of frequent objects --- subject to particular filtering and sorting --- are available. We call the corresponding interactive tools Element, Path, and Couple Analyzers.We also allow in-depth exploration of individual elements, paths, and couples: Element Explorer investigates composition and convergence of traffic through an element and allows conditioning based on the number of preceding/succeeding steps. Path Explorer visualizes in and out flows of a path and attrition rate along the path. Couple Explorer presents distinct paths connecting couple elements, along with measures of their association and some additional statistics.
Fast ordering of large categorical datasets for better visualization
Authors: Alina Beygelzimer, Chang-Shing Perng, Sheng Ma
Venue: KDD
Year: 2001

An important issue in visualizing categorical data is how to order categorical values. The focus of this paper is on constructing such orderings efficiently without compromising their visual quality.
Random projection in dimensionality reduction: applications to image and text data
Authors: Ella Bingham, Heikki Mannila
Venue: KDD
Year: 2001

Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however, empirical results are sparse. We present experimental results on using random projection as a dimensionality reduction tool in a number of cases, where the high dimensionality of the data would otherwise lead to burden-some computations. Our application areas are the processing of both noisy and noiseless images, and information retrieval in text documents. We show that projecting the data onto a random lower-dimensional subspace yields results comparable to conventional dimensionality reduction methods such as principal component analysis: the similarity of data vectors is preserved well under random projection. However, using random projections is computationally significantly less expensive than using, e.g., principal component analysis. We also show experimentally that using a sparse random matrix gives additional computational savings in random projection.
Data mining criteria for tree-based regression and classification
Authors: Andreas Buja, Yung-Seop Lee
Venue: KDD
Year: 2001

This paper is concerned with the construction of regression and classification trees that are more adapted to data mining applications than conventional trees. To this end, we propose new splitting criteria for growing trees. Conventional splitting criteria attempt to perform well on both sides of a split by attempting a compromise in the quality of fit between the left and the right side. By contrast, we adopt a data mining point of view by proposing criteria that search for interesting subsets of the data, as opposed to modeling all of the data equally well. The new criteria do not split based on a compromise between the left and the right bucket; they effectively pick the more interesting bucket and ignore the other.As expected, the result is often a simpler characterization of interesting subsets of the data. Less expected is that the new criteria often yield whole trees that provide more interpretable data descriptions. Surprisingly, it is a "flaw" that works to their advantage: The new criteria have an increased tendency to accept splits near the boundaries of the predictor ranges. This so-called "end-cut problem" leads to the repeated peeling of small layers of data and results in very unbalanced but highly expressive and interpretable trees.
Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction
Authors: Igor V. Cadez, Padhraic Smyth, Heikki Mannila
Venue: KDD
Year: 2001

Transaction data is ubiquitous in data mining applications. Examples include market basket data in retail commerce, telephone call records in telecommunications, and Web logs of individual page-requests at Web sites. Profiling consists of using historical transaction data on individuals to construct a model of each individual's behavior. Simple profiling techniques such as histograms do not generalize well from sparse transaction data. In this paper we investigate the application of probabilistic mixture models to automatically generate profiles from large volumes of transaction data. In effect, the mixture model represents each individual's behavior as a linear combination of "basis transactions." We evaluate several variations of the model on a large retail transaction data set and show that the proposed model provides improved predictive power over simpler histogram-based techniques, as well as being relatively scalable, interpretable, and flexible. In addition we point to applications in outlier detection, customer ranking, interactive visualization, and so forth. The paper concludes by comparing and relating the proposed framework to other transaction-data modeling techniques such as association rules.
Gaining insights into support vector machine pattern classifiers using projection-based tour methods
Authors: Doina Caragea, Dianne Cook, Vasant G. Honavar
Venue: KDD
Year: 2001

This paper discusses visual methods that can be used to understand and interpret the results of classification using support vector machines (SVM) on data with continuous real-valued variables. SVM induction algorithms build pattern classifiers by identifying a maximal margin separating hyperplane from training examples in high dimensional pattern spaces or spaces induced by suitable nonlinear kernel transformations over pattern spaces. SVM have been demonstrated to be quite effective in a number of practical pattern classification tasks. Since the separating hyperplane is defined in terms of more than two variables it is necessary to use visual techniques that can navigate the viewer through high-dimensional spaces. We demonstrate the use of projection-based tour methods to gain useful insights into SVM classifiers with linear kernels on 8-dimensional data.
PVA: a self-adaptive personal view agent system
Authors: Chien Chin Chen, Meng Chang Chen, Yeali S. Sun
Venue: KDD
Year: 2001

In this paper, we present PVA, an adaptive personal view information agent system to track, learn and manage, user's interests in Internet documents. When user's interests change, PVA, in not only the contents, but also in the structure of user profile, is modified to adapt to the changes. Experimental results show that modulating the structure of user profile does increase the accuracy of personalization systems.