Searching frequent patterns in transactional databases is considered as one of the most
important data mining problems and Apriori is one of the typical algorithms for this task …

Linked data has experienced accelerated growth in recent years. With the continuing
proliferation of structured data, demand for RDF compression is becoming increasingly …

Itemset mining is a well-known exploratory technique used to discover interesting
correlations hidden in a data collection. Since ever increasing amounts of data are being …

Large volumes of data are being produced by various modern applications at an ever
increasing rate. These applications range from wireless sensors networks to social …

As an important part of discovering association rules, frequent itemsets mining plays a key
role in mining associations, correlations, causality and other important data mining tasks …

摘要关联规则挖掘算法FP Growth 虽然效率比Apriori 要快一个数量级, 但存在频繁模式树可能
过大而内存无法容纳和数据挖掘过程串行处理等两大缺点. 提出一种分布式并行关联规则挖掘 …

Frequent pattern mining is an important problem in data mining with many practical
applications. Current parallel methods for mining frequent patterns unstably perform for …

In view of the traditional parallel FP-growth algorithm (PFP) that suffers from two major
limitations, namely, multiple database scans requirement (ie, high I/O cost) and high inter …

In this paper, we present a new approach relevant to the discovery of correlated patterns,
based on the use of multicore architectures. Our work rests on a full KDD system and allows …

Nearest Neighbor Search problem is an important research topic in data mining field. In this
paper, we discuss our continuous work on finding nearest neighbors in multi-dimensional …