Search Results for "frequent mining"

When data mining techniques are applied to discover useful knowledge behind a large data collection, they are required to be able to preserve some confidential information, such as sensitive frequent itemsets, rules and the like. A feasible way to ensure the confidentiality is to sanitize the database and conceal sensitive information. However, the sanitization process often produces side effects, thus minimizing these side effects is an important task.

Incremental data mining algorithms process frequent up- dates to dynamic datasets efficiently by avoiding redundant computa- tion. Existing incremental extension to shared nearest neighbor density based clustering (SNND) algorithm cannot handle deletions to dataset and handles insertions only one point at a time. We present an incremen- tal algorithm to overcome both these bottlenecks by efficiently identify- ing affected parts of clusters while processing updates to dataset in batch mode.

The Apriori Algorithm is an influential algorithm for mining frequent itemsets for boolean association rules. Key Concepts : • Frequent Itemsets: The sets of item which has minimum support (denoted by Li for i th -Itemset). • Apriori Property: Any subset of frequent itemset must be frequent. • Join Operation: To find Lk , a set of candidate k-itemsets is generated by joining Lk-1 with itself.

In this project, we tackle the calculation of frequent items in a data stream, and show how it can be implemented using GPUs.

This open source project is aimed to create an efficient XML frequent pattern mining tool,which includes four main functions, TreeMining, Stream Mining, Sequence Mining and Version Mining.

This project aims to create a method able to determine the most frequent word phrases in a large source of text data (>5 Gb) using the computational power of multiple processors.

Data mining tool for the extraction of spatio-temporal frequent patterns ("Trajectory patterns" or "T-patterns") from GPS-like trajectories of a set of moving objects. Work performed within the European project GeoPKDD - www.geopkdd.eu