Search Results for "frequent subgraph 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.

This is an optimized Java implementation of the algorithm from "LTS: Discriminative Subgraph Mining by Learning from Search History" in Data Engineering (ICDE), IEEE 27th International Conference, pages 207-218, 2011. The Learning to Search (LTS) algorithm mines for discriminative subgraphs. Given two sets of graphs, with one set labelled positive, it finds subgraphs that are common in the positive graphs and uncommon in the negative graphs. In other words, the occurrences of these subgraphs...

..., the total worst-case algorithm complexity is O(m^n * n(n-1)/2 * km * log m). This Java implementation implements our ASM algorithm. See README file: https://sourceforge.net/projects/asmalgorithm/files/ If you use our ASM implementation to support academic research, please cite the following paper: Haibin Liu, Lawrence Hunter, Vlado Keselj, and Karin Verspoor. Approximate Subgraph Matching-based Literature Mining for Biomedical Events and Relations. PLOS ONE, 8:4 e60954, 2013.

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...

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