Mining closed frequent item set (CFI) plays a fundamental role in many real-world data
mining applications. However, memory requirement and computational cost have become …

Distributed data mining implements techniques for analyzing data on distributed computing
systems by exploiting data distribution and parallel algorithms. The grid is a computing …

Methods and systems are provided for partitioning data of a database or data store into
several independent parts as part of a data mining process. The methods and systems use a …

Mining frequent patterns is to discover the groups of items appearing always together
excess of a user specified threshold. Many approaches have been proposed for mining …

In the era of “Big Data”, there is an emerging need to process a massive data set using large
cluster system. Anyway, without the right strategies to handle the data, it is challenging to …

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

Although the FP-Growth association-rule mining algorithm is more efficient than the Apriori
algorithm, it has two disadvantages. The first is that the FP-tree can become too large to be …

本文提出了一种云环境下基于二进制编码的并行频繁项集挖掘算法, 利用一种特殊的二进制编码
的依赖度计量方法对原始数据集合进行编码转换及依赖度聚类, 然后将数据集分布部署在云环境 …

In contemporary internet architectures, including server farms and blog aggregators, web log
data may be scattered among multiple cooperating peers. In order to perform content …

Although the association rules mining algorithm FP-Growth is more efficient than Apriori, it
has two disadvantages. The first one is that the FP-tree could be too large to be created in …