Recently, high utility pattern (HUP) mining is one of the most important research issues in
data mining due to its ability to consider the nonbinary frequency values of items in …

In electronic commerce web sites, recommender systems are popularly being employed to
help customers in selecting suitable products to meet their personal needs. These systems …

Finding frequent patterns in a continuous stream of transactions is critical for many
applications such as retail market data analysis, network monitoring, web usage mining, and …

The FP-growth algorithm using the FP-tree has been widely studied for frequent pattern
mining because it can dramatically improve performance compared to the candidate …

Data mining is used for mining useful data from huge datasets and finding out meaningful
patterns from the data. Many organizations are now using data mining techniques. Frequent …

High utility pattern (HUP) mining over data streams has become a challenging research
issue in data mining. When a data stream flows through, the old information may not be …

Automatic text summarization is the process of shortening a large document into a summary
text that preserves the main concepts and key points of the original document. Due to the …

Weighted frequent pattern (WFP) mining is more practical than frequent pattern mining
because it can consider different semantic significance (weight) of the items. For this reason …

This paper proposes an efficient method, the frequent items ultrametric trees (FIUT), for
mining frequent itemsets in a database. FIUT uses a special frequent items ultrametric tree …

In this study, it proposes a new optimization algorithm called APRIORI-IMPROVE based on
the insufficient of Apriori. APRIORI-IMPROVE algorithm presents optimizations on 2-items …