Search Results for "frequent subgraph mining"

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

LTS (Learning to Search) is an implementation of an algorithm described in "LTS: Discriminative Subgraph Mining by Learning from Search History" in Data Engineering (ICDE), IEEE 27th International Conference, pages 207-218, 2011. The purpose of LTS is to find discriminative subgraphs, which are smaller graphs that are embedded in larger graphs that all share a certain trait. A discriminative subgraph can help to characterize a complex graph and can be used to classify new graphs with unknown...

A data mining tool for finding frequent motifs in DNA regulatory area that may play significant role in gene regulation. It makes the search more efficient by using different data like conservation and binding scores about relevant promoter areas. It is implemented as a part of Barchelor thesis work of Timo Petmanson available here: http://comserv.cs.ut.ee/forms/ati_report/downloader.php?file=0701eb033c3bad8d3be4a79a29c70107c1cad745

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

baobab is an implementation of FPTrees or Frequent Pattern Trees, a pattern recognition/data mining technique. it has innumerable applications in language processing, clickstream analysis, etc.