scholarly journals Using Frequent Closed Pattern Mining to Solve a Consensus Clustering Problem

2016 ◽  
Author(s):  
Atheer Al-Najdi ◽  
Nicolas Pasquier ◽  
Frederic Precioso
Keyword(s):  
Pattern Mining ◽  
Consensus Clustering ◽  
Clustering Problem ◽  
Closed Pattern ◽  
Closed Pattern Mining

Novel techniques and an efficient algorithm for closed pattern mining

2014 ◽  
Vol 41 (11) ◽  
pp. 5105-5114 ◽  
Author(s):  
András Király ◽  
Asta Laiho ◽  
János Abonyi ◽  
Attila Gyenesei
Keyword(s):  
Efficient Algorithm ◽  
Pattern Mining ◽  
Closed Pattern ◽  
Closed Pattern Mining

Using Closed Patterns to Solve the Consensus Clustering Problem

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1379-1397 ◽  
Author(s):  
Atheer Al-Najdi ◽  
Nicolas Pasquier ◽  
Frédéric Precioso
Keyword(s):  
Pattern Mining ◽  
Clustering Algorithms ◽  
Search Space ◽  
Consensus Clustering ◽  
Clustering Method ◽  
Clustering Problem ◽  
Mining Technique

Clustering is the process of partitioning a dataset into groups based on the similarity between the instances. Many clustering algorithms were proposed, but none of them proved to provide good quality partition in all situations. Consensus clustering aims to enhance the clustering process by combining different partitions obtained from different algorithms to yield a better quality consensus solution. In this work, we propose a new consensus clustering method that uses a pattern mining technique in order to reduce the search space from instance-based into pattern-based space. Instead of finding one solution, our method generates multiple consensus candidates based on varying the number of base clusterings considered. The different solutions are then linked and presented as a tree that gives more insight about the similarities between the instances and the different partitions in the ensemble.

Distributed Mining of Closed Patterns from Multi-Relational Data

2015 ◽  
Vol 19 (6) ◽  
pp. 804-809 ◽  
Author(s):  
Yohei Kamiya ◽  
◽  
Hirohisa Seki
Keyword(s):  
Inductive Logic Programming ◽  
Formal Concept Analysis ◽  
Relational Databases ◽  
Pattern Mining ◽  
Inductive Logic ◽  
Relational Data ◽  
Formal Concept ◽  
Global Database ◽  
Closed Pattern ◽  
Closed Pattern Mining

In multi-relational data mining (MRDM), there have been proposed many methods for searching for patterns that involve multiple tables (relations) from a relational database. In this paper, we consider closed pattern mining from distributed multi-relational databases (MRDBs). Since the computation of MRDM is costly compared with the conventional itemset mining, we propose some efficient methods for computing closed patterns using the techniques studied in Inductive Logic Programming (ILP) and Formal Concept Analysis (FCA). Given a set oflocaldatabases, we first compute sets of their closed patterns (concepts) using a closed pattern mining algorithm tailored to MRDM, and then generate the set of closed patterns in the global database by utilizing themergeoperator. We also present some experimental results, which shows the effectiveness of the proposed methods.

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