KDD

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, ...

Tom M. Mitchell is author of the textbook "Machine Learning" (McGraw Hill, 1997), President of the American Association for Artificial Intelligence and a member of the National Research Council's Computer Science and Telecommunications Board. He is Vice ...

Mass Collaboration is a new "P2P"-style approach to large-scale knowledge sharing, with applications in customer support, focused community development, and capturing knowledge distributed within large organizations. Effectively supporting this paradigm ...

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. ...

Data mining started its move out of the statistics and machine learning ghettos and into the mainstream almost 10 years ago. With great fanfare and a large influx of venture capital, data mining was going to change the very nature of business. Yet data ...

Organizations conducting Electronic Commerce (e-commerce) can greatly benefit from the insight that data mining of transactional and clickstream data provides. Such insight helps not only to improve the electronic channel (e.g., a web site), but it is ...

Recommender systems have been revolutionizing the way shoppers and information seekers find what they want. We will study some of the tremendous successes and spectacular failures of recommenders in E-commerce to understand the causes of the success or ...

Skewed distributions appear very often in practice. Unfortunately, the traditional Zipf distribution often fails to model them well. In this paper, we propose a new probability distribution, the Discrete Gaussian Exponential (DGX), to achieve excellent ...

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 ...

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 ...

The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples (x, y) that are within a distance ε.One of the most efficient algorithms for processing similarity-...

One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected profit from a customer is greater than the cost of marketing to her, the marketing action for that customer is ...

This paper considers the framework of the so-called "market basket problem", in which a database of transactions is mined for the occurrence of unusually frequent item sets. In our case, "unusually frequent" involves estimates of the frequency of each ...

Instead of a standard support vector machine (SVM) that classifies points by assigning them to one of two disjoint half-spaces, points are classified by assigning them to the closest of two parallel planes (in input or feature space) that are pushed ...

Recently we presented a new approach [18] to the classification problem arising in data mining. It is based on the regularization network approach but, in contrast to other methods which employ ansatz functions associated to data points, we use a grid ...

Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months ...

Interactive visualizations are effective tools in mining scientific, engineering, and business data to support decision-making activities. Star Coordinates is proposed as a new multi-dimensional visualization technique, which supports various ...

The problem of similarity search (query-by-content) has attracted much research interest. It is a difficult problem because of the inherently high dimensionality of the data. The most promising solutions involve performing dimensionality reduction on ...

For many KDD operations, such as nearest neighbor search, distance-based clustering, and outlier detection, there is an underlying κ-D data space in which each tuple/object is represented as a point in the space. In the presence of differing scales, ...

We present the application of Feature Mining techniques to the Developmental Therapeutics Program's AIDS antiviral screen database. The database consists of 43576 compounds, which were measured for their capability to protect human cells from HIV-1 ...

Ever since the beginning of the Web, finding useful information from the Web has been an important problem. Existing approaches include keyword-based search, wrapper-based information extraction, Web query and user preferences. These approaches ...

Clickstream data collected at any web site (site-centric data) is inherently incomplete, since it does not capture users' browsing behavior across sites (user-centric data). Hence, models learned from such data may be subject to limitations, the nature ...

We investigate the application of Bayesian networks, Markov random fields, and mixture models to the problem of query answering for transaction data sets. We formulate two versions of the querying problem: the query selectivity estimation (i.e., finding ...

Game sites on the World Wide Web draw people from around the world with specialized interests, skills, and knowledge. Data from the games often reflects the players' expertise and will to win. We extract probabilistic forecasts from data obtained from ...

We focus on the problem of finding patterns across two large, multidimensional datasets. For example, given feature vectors of healthy and of non-healthy patients, we want to answer the following questions: Are the two clouds of points separable? What ...

We present a generalization of frequent itemsets allowing for the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifies error-tolerant frequent clusters of items in transactional data (...

In many data mining domains, misclassification costs are different for different examples, in the same way that class membership probabilities are example-dependent. In these domains, both costs and probabilities are unknown for test examples, so both ...

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 ...

High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Therefore, techniques have recently been proposed to find clusters in hidden subspaces of the data. However, since the behavior ...