Data mining of vector-item patterns using neighborhood histograms

作者：Denton Anne M^*; Wu Jianfei

来源：Knowledge and Information Systems, 2009, 21(2): 173-199.

DOI：10.1007/s10115-009-0201-7

• Pattern mining
• Pattern significance
• Significance of classification
• Vector space representation
• Gene expression analysis
• Time series subsequences

The representation of multiple continuous attributes as dimensions in a vector space has been among the most influential concepts in machine learning and data mining. We consider sets of related continuous attributes as vector data and search for patterns that relate a vector attribute to one or more items. The presence of an item set defines a subset of vectors that may or may not show unexpected density fluctuations. We test for fluctuations by studying density histograms. A vector-item pattern is considered significant if its density histogram significantly differs from what is expected for a random subset of transactions. Using two different density measures, we evaluate the algorithm on two real data sets and one that was artificially constructed from time series data.

• 出版日期2009-11