Class membership probability estimates are important for many applications of data mining in which classification outputs are combined with other sources of information for decision-making, such as example-dependent misclassification costs, the outputs ...

Existing axis scaling and dimensionality methods focus on preserving structure, usually determined via the Euclidean distance. In other words, they inherently assume that the Euclidean distance is already correct. We instead propose a novel nonlinear ...

This paper provides a blueprint for constructing collaborative and distributed knowledge discovery systems within Grid-based computing environments. The need for such systems is driven by the quest for sharing knowledge, information and computing ...

A fundamental problem in business and other applications is ranking items with respect to some notion of profit based on historical transactions. The difficulty is that the profit of one item not only comes from its own sales, but also from its ...

In this paper, we address the problem of detecting multiple topics or categories of text where each text is not assumed to belong to one of a number of mutually exclusive categories. Conventionally, the binary classification approach has been employed, ...

Data clustering has attracted a lot of research attention in the field of computational statistics and data mining. In most related studies, the dissimilarity between two clusters is defined as the distance between their centroids, or the distance ...

Developing regression models for large datasets that are both accurate and easy to interpret is a very important data mining problem. Regression trees with linear models in the leaves satisfy both these requirements, but thus far, no truly scalable ...

This paper introduces FAST, a novel two-phase sampling-based algorithm for discovering association rules in large databases. In Phase I a large initial sample of transactions is collected and used to quickly and accurately estimate the support of each ...

Many enterprises incorporate information gathered from a variety of data sources into an integrated input for some learning task. For example, aiming towards the design of an automated diagnostic tool for some disease, one may wish to integrate data ...

Heterogeneous types of gene expressions may provide a better insight into the biological role of gene interaction with the environment, disease development and drug effect at the molecular level. In this paper for both exploring and prediction purposes ...

A key challenge facing IT organizations today is their evolution towards adopting e-business practices that gives rise to the need for reengineering their underlying software systems. Any reengineering effort has to be aware of the functional ...

Handling massive datasets is a difficult problem not only due to prohibitively large numbers of entries but in some cases also due to the very high dimensionality of the data. Often, severe feature selection is performed to limit the number of ...

Boosting is a strong ensemble-based learning algorithm with the promise of iteratively improving the classification accuracy using any base learner, as long as it satisfies the condition of yielding weighted accuracy > 0.5. In this paper, we analyze ...

We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transactions. While it is feasible to recover association rules and preserve ...

In this paper we investigate the general problem of discovering recurrent patterns that are embedded in categorical sequences. An important real-world problem of this nature is motif discovery in DNA sequences. We investigate the fundamental aspects of ...

This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant ...

In the last several years, large OLAP databases have become common in a variety of applications such as corporate data warehouses and scientific computing. To support interactive analysis, many of these databases are augmented with hierarchical ...

Constraint-based mining of itemsets for questions such as "find all frequent itemsets where the total price is at least $50" has received much attention recently. Two classes of constraints, monotone and antimonotone, have been identified as very ...

Support Vector Machines and other kernel methods have proven to be very effective for nonlinear inference. Practical issues are how to select the type of kernel including any parameters and how to deal with the computational issues caused by the fact ...

Covariance and correlation estimates have important applications in data mining. In the presence of outliers, classical estimates of covariance and correlation matrices are not reliable. A small fraction of outliers, in some cases even a single outlier, ...