scholarly journals From Local Pattern Mining to Relevant Bi-cluster Characterization

2005 ◽  
pp. 293-304 ◽  
Author(s):  
Ruggero G. Pensa ◽  
Jean-François Boulicaut
Keyword(s):  
Pattern Mining ◽  
Local Pattern

scholarly journals SIAS-miner: mining subjectively interesting attributed subgraphs

2019 ◽  
Vol 34 (2) ◽  
pp. 355-393 ◽  
Author(s):  
Anes Bendimerad ◽  
Ahmad Mel ◽  
Jefrey Lijffijt ◽  
Marc Plantevit ◽  
Céline Robardet ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Clustering ◽  
Pattern Mining ◽  
Prior Work ◽  
Abstract Form ◽  
Trade Off ◽  
Local Pattern ◽  
Attribute Data ◽  
Subgraph Mining ◽  
Definition Of

AbstractData clustering, local pattern mining, and community detection in graphs are three mature areas of data mining and machine learning. In recent years, attributed subgraph mining has emerged as a new powerful data mining task in the intersection of these areas. Given a graph and a set of attributes for each vertex, attributed subgraph mining aims to find cohesive subgraphs for which (some of) the attribute values have exceptional values. The principled integration of graph and attribute data poses two challenges: (1) the definition of a pattern syntax (the abstract form of patterns) that is intuitive and lends itself to efficient search, and (2) the formalization of the interestingness of such patterns. We propose an integrated solution to both of these challenges. The proposed pattern syntax improves upon prior work in being both highly flexible and intuitive. Plus, we define an effective and principled algorithm to enumerate patterns of this syntax. The proposed approach for quantifying interestingness of these patterns is rooted in information theory, and is able to account for background knowledge on the data. While prior work quantified the interestingness for the cohesion of the subgraph and for the exceptionality of its attributes separately, then combining these in a parameterized trade-off, we instead handle this trade-off implicitly in a principled, parameter-free manner. Empirical results confirm we can efficiently find highly interesting subgraphs.

Supervised Local Pattern Mining

2016 ◽  
pp. 141-161 ◽  
Author(s):  
Sebastián Ventura ◽  
José María Luna
Keyword(s):  
Pattern Mining ◽  
Local Pattern

An Adaptive Data Distribution Through Tree Rules in Frequent Pattern Mining

2018 ◽  
pp. 300-305
Keyword(s):  
Information Sharing ◽  
Pattern Mining ◽  
Data Distribution ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
General Development ◽  
Secure Information ◽  
Evaluation Parameters ◽  
Secure Information Sharing

Information sharing among the associations is a general development in a couple of zones like business headway and exhibiting. As bit of the touchy principles that ought to be kept private may be uncovered and such disclosure of delicate examples may impacts the advantages of the association that have the data. Subsequently the standards which are delicate must be secured before sharing the data. In this paper to give secure information sharing delicate guidelines are bothered first which was found by incessant example tree. Here touchy arrangement of principles are bothered by substitution. This kind of substitution diminishes the hazard and increment the utility of the dataset when contrasted with different techniques. Examination is done on certifiable dataset. Results shows that proposed work is better as appear differently in relation to various past strategies on the introduce of evaluation parameters.

Review of Improvement of Web Search Based on Web Log File

2012 ◽  
Vol 3 (2) ◽  
pp. 298-300 ◽  
Author(s):  
Soniya P. Chaudhari ◽  
Prof. Hitesh Gupta ◽  
S. J. Patil
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Web Search ◽  
Pattern Mining ◽  
Efficiency Improvement ◽  
Web Searching ◽  
Important Method ◽  
Web Log ◽  
Journal Paper ◽  
Log File

In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

Sign in / Sign up

Export Citation Format

Share Document