TABLE OF CONTENTS Wei Wang
Department of Computer Science, University of North Carolina at Chapel Hill
Jiong Yang
Department of Electronic Engineering and Computer Science, Case Western Reserve University
| Abstract | With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the high dimension space. In this chapter, we present several state-of-art techniques for analyzing high-dimensional data, e.g., frequent pattern mining, clustering, and classification. We will discuss how these methods deal with the challenges of high dimensionality. |
| Keywords: | High-dimensional Data Mining, frequent pattern, clustering high-dimensional data, classifying high-dimensional data |
The emergence of various new application domains, such as bioinformatics and e-commerce, underscores the need for analyzing high dimensional data. In a gene expression microarray data set, there could be tens or hundreds of dimensions, each of which corresponds to an experimental condition. In a customer purchase behavior data set, there may be up to hundreds of thousands of merchandizes, each of which is mapped to a dimension. Researchers and practitioners are very eager in analyzing these data sets.
Various Data Mining models have been proven to be very successful for analyzing very large data sets. Among them, frequent patterns, clusters, and classifiers are three widely studied models to represent, analyze, and summarize large data sets.
