Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information. It can be defined as the activity that extracts information contained in very large database. That information can be used to increase the revenue or cut costs. Association Rule Mining (ARM) is finding out the frequent itemsets or patterns among the existing items from the given ...

We proposed a new algorithm SuffixMiner which eliminates the requirement of multiple passes through the data when finding out all frequent itemsets in data streams, takes full advantage of the special property of suffixtree to avoid generating candidate itemsets and traversing each suffix-tree during the itemset growth, and utilizes a new itemset growth method to mine all frequent itemsets in d...

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of...

Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data. Hence, efficient algorithms are required to process such large amounts of data. In recent ye...

Efficient discovery of frequent patterns from large databases is an active research area in data mining with broad applications in industry and deep implications in many areas of data mining. Although many efficient frequent-pattern mining techniques have been developed in the last decade, most of them assume relatively small databases, leaving extremely large but realistic datasets out of reac...