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A Method to Encode Heterogeneous Information Sources for Contextual Pattern Mining

IP.com Disclosure Number: IPCOM000227447D
Publication Date: 2013-May-08

Publishing Venue

The IP.com Prior Art Database

Abstract

Traditional pattern mining methods usually work on single data sources. However, in practice, there are often multiple and heterogeneous information sources. They collectively provide contextual information not available in any single source alone describing the same set of objects, and are useful for discovering hidden contextual patterns. One important challenge is to provide a general methodology to mine contextual patterns easily and efficiently. A general framework to encode contextual information from multiple sources into a coherent representation---Contextual Information Graph (CIG), is proposed in this article. The complexity of the encoding scheme is linear in both time and space. More importantly, CIG can be handled by any single-source pattern mining algorithms that accept taxonomies without any modification. Contextual patterns providing rich and insightful knowledge can be easily discovered by the proposed framework. It enables Contextual Pattern Mining (CPM) by reusing single-source methods, and is easy to deploy and use in real-world systems.

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A Method to Encode Heterogeneous Information Sources for Contextual Pattern Mining

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