Interval Set Representations of Clusters

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Author(s): Pawan Lingras (Saint Mary’s University, Canada), Rui Yan (Saint Mary’s University, Canada), Mofreh Hogo (Czech Technical University, Czech Republic)and Chad West (IBM Canada Limited, Canada)
Copyright: 2005
Pages: 5
Source title: Encyclopedia of Data Warehousing and Mining
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59140-557-3.ch125

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

The amount of information that is available in the new information age has made it necessary to consider various summarization techniques. Classification, clustering, and association are three important data-mining features. Association is concerned with finding the likelihood of co-occurrence of two different concepts. For example, the likelihood of a banana purchase given that a shopper has bought a cake. Classification and clustering both involve categorization of objects. Classification processes a previously known categorization of objects from a training sample so that it can be applied to other objects whose categorization is unknown. This process is called supervised learning. Clustering groups objects with similar characteristics. As opposed to classification, the grouping process in clustering is unsupervised. The actual categorization of objects, even for a sample, is unknown. Clustering is an important step in establishing object profiles.

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Interval Set Representations of Clusters

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