Xiaoyong Lin

Learn More
Mining frequent patterns has attracted considerable attention in the data mining field. Most of the current studies adopt the pattern growth approach of divide-and-conquer. However, as the mining process is completely split into parts, all relevant algorithms still encounter some performance bottlenecks. In this study, we propose a new data structure,(More)
The paper proposes a new way of comprising the Non-negative matrix factorization (NMF) and Testor theory to make topic discovery. NMF method is good at dealing with high dimensional documents and clustering, while Testor theory is used to find the topic of each cluster. By an example of ten abstracts of Chinese science literature from magazines relative to(More)
Mining frequent patterns has been studied popularly in data mining research. However, very little work has been done on maintenance of mined frequent patterns. For the real useful frequent patterns, one must continually adjust a minimum support threshold. Expensive and repeated database scans were done. A novel incremental updating frequent pattern tree(More)
Online event detection techniques are usually used in single data source. This paper analyzes event detection in the perspective of multiple data sources, combining news reports and microblogs. Detect events from news, combining microblogs to do event monitoring and early warning. Also improve feature selection methods for multiple data sources event(More)
  • 1