Interestingness measures play an important role in data mining, regardless of the kind of
patterns being mined. These measures are intended for selecting and ranking patterns …

The task of finding correlations between items in a dataset, association mining, has received
considerable attention over the last decade. This article presents a survey of association …

We introduce a new kind of patterns, called emerging patterns (EPs), for knowledge
discovery from databases. EPs are defined as itemsets whose supports increase …

The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and
Spatiotemporal DatabasesSince the publication of the first edition of Geographic Data …

During decision making, we are often confronted by a huge amount of factors. These factors
may be either an advantage or a disadvantage to a decision objective. For the purpose of …

Having access to massive amounts of data does not necessarily imply that induction
algorithms must use them all. Samples often provide the same accuracy with far less …

This valuable text addresses different pattern recognition (PR) tasks in a unified framework
with both theoretical and experimental results. Tasks covered include data condensation …

This paper proposes an alternative to ROC representation, in which the expected cost of a
classifier is represented explicitly. This expected cost representation maintains many of the …

Knowledge Discovery and Measures of Interest is a reference book for knowledge discovery
researchers, practitioners, and students. The knowledge discovery researcher will find that …

Abstract Motivations and Results: Gene groups that are significantly related to a disease can
be detected by conducting a series of gene expression experiments. This work is aimed at …