Automatic Pattern-Taxonomy Extraction for Web Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Frequent pattern discovery in online environment
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Mining frequent tree-like patterns in large datasets
Data & Knowledge Engineering
Mining positive and negative patterns for relevance feature discovery
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
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Sequential patterns discovery has emerged as animportant problem in data mining. In this paper, wepropose an effective GST algorithm for mining sequentialpatterns in a large transaction database. Different from theApriori-like algorithms, the GST algorithm can out oforder find large k-sequences (k = 3); i.e., we can findlarge k-sequences not directly through large(k-1)-sequences. This leads to that our algorithm has muchbetter performance than the Apriori-like algorithms.Besides, we also propose the method to find newsequential patterns by scanning only new transactionssince the database was increased. Through severalcomprehensive experiments, the GST algorithm gains asignificant performance improvement over the Apriori-likealgorithms. Also we found as long as the ratio of the itemspurchased in new transactions is not close to 100%,scanning only new transactions is always much better thanscanning the entire database.