NDLI: Density-Based Clustering of Massive Short Messages Using Domain Ontology

Content Provider	IEEE Xplore Digital Library
Author	Shenghong Yang Yongheng Wang
Copyright Year	2009
Abstract	With the rapid development of information technology, huge data is accumulated. A vast amount of such data appears as short messages such as emails in companies or conversations in open chatting rooms. It is useful to find the themes or exceptional information from the messages by clustering the short documents based on density. However, traditional vector space model based text clustering algorithms can not get acceptable accuracy because the key words appear at low frequency. On the other hand, traditional text clustering algorithms become very inefficient or even unavailable when processing massive data at TB level. In this paper a density-based short message clustering algorithm using domain ontology based is presented. This algorithm uses domain ontology to calculate the semantic similarity between short messages which improves the Parallel method is also used to get better scalability.
Starting Page	505
Ending Page	508
File Size	261259
Page Count	4
File Format	PDF
ISBN	9780769536996
DOI	10.1109/APCIP.2009.260
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering methods Scalability text clustering density Ontologies Partitioning algorithms Computer science Databases massive short messages Clustering algorithms Information processing Frequency Optical noise domain ontology
Content Type	Text
Resource Type	Article

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