Fast parallel and serial approximate string matching
Journal of Algorithms
WebACE: a Web agent for document categorization and exploration
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Self-Organising Maps for Hierarchical Tree View Document Clustering Using Contextual Information
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic model for clustering text documents with multiple fields
ECIR'07 Proceedings of the 29th European conference on IR research
Hi-index | 0.00 |
Classical text clustering algorithms are usually based on vector space model or its variants. Because of the high computing complexity and the difficulty of controlling clustering results, this kind of approaches are hard to be applied for the purpose of the large scale text clustering, Clustering algorithms based on frequent term sets make use of relationship among documents and their shared frequent term sets to achieve high accuracy and effectiveness in clustering. But since the number of frequent terms is usually too large to reach the efficiency requirement for large collection texts clustering, this paper proposes a novel text clustering approach based on maximal frequent term sets (MFTSC). This approach firstly mines maximal frequent term sets from text set and then clusters texts by following steps: at first, the maximal frequent term sets are clustered based on the criterion of k-mismatch; then texts are clustered according to term sets clustering results; finally, we categorize the left texts uncovered in previous step into produced text clusters Be compared with existing approaches, our experimental results show an average gain of 10% on F-Measure score with better performance on scalability and efficiency.