Clustering Text Data Streams

Abstract Clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organization and topic detection and tracing etc. However, most methods are similarity-based approaches and only use the TFIDF scheme to represent the semantics of text data and often lead to poor clustering quality. Recently, researchers argue that semantic smoothing model is more efficient than the existing TFIDF scheme for improving text clustering quality. However, the existing semantic smoothing model is not suitable for dynamic text data context. In this paper, we extend the semantic smoothing model into text data streams context firstly. Based on the extended model, we then present two online clustering algorithms OCTS and OCTSM for the clustering of massive text data streams. In both algorithms, we also present a new cluster statistics structure named cluster profile which can capture the semantics of text da...

• Type: Journal
• Year: 2008
• Where: JCST
• Authors: Yubao Liu, Jiarong Cai, Jian Yin, Ada Wai-Chee Fu
• Keywords: JCST 2008, Semantic Smoothing Model, Semantics, Text Data