Optimized Rule Mining Through a Unified Framework for Interestingness Measures

2006 ◽  
pp. 238-247 ◽  
Author(s):  
Céline Hébert ◽  
Bruno Crémilleux
Keyword(s):  
Unified Framework ◽  
Rule Mining ◽  
Interestingness Measures

scholarly journals Finding Efficient Positive and Negative Itemsets Using Interestingness Measures

2018 ◽  
Vol 7 (4.36) ◽  
pp. 533
Author(s):  
P. Asha ◽  
T. Prem Jacob ◽  
A. Pravin
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Data Gathering ◽  
Unstructured Data ◽  
Rule Mining ◽  
Interestingness Measures ◽  
Data Mining Algorithms ◽  
Data Formats ◽  
Mining Algorithms ◽  
Rare Itemsets

Currently, data gathering techniques have increased through which unstructured data creeps in, along with well defined data formats. Mining these data and bringing out useful patterns seems difficult. Various data mining algorithms were put forth for this purpose. The associated patterns generated by the association rule mining algorithms are large in number. Every ARM focuses on positive rule mining and very few literature has focussed on rare_itemsets_mining. The work aims at retrieving the rare itemsets that are of most interest to the user by utilizing various interestingness measures. Both positive and negative itemset mining would be focused in this work.  

Analysis of Various Interestingness Measures in Class Association Rule Mining

2011 ◽  
Vol 4 (4) ◽  
pp. 295-304 ◽  
Author(s):  
Xianneng LI ◽  
Shingo MABU ◽  
Huiyu ZHOU ◽  
Kaoru SHIMADA ◽  
Kotaro HIRASAWA
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Interestingness Measures ◽  
Class Association Rule

Behavior-based clustering and analysis of interestingness measures for association rule mining

2013 ◽  
Vol 28 (4) ◽  
pp. 1004-1045 ◽  
Author(s):  
C. Tew ◽  
C. Giraud-Carrier ◽  
K. Tanner ◽  
S. Burton
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Interestingness Measures ◽  
Behavior Based

Re-examination of interestingness measures in pattern mining: a unified framework

2010 ◽  
Vol 21 (3) ◽  
pp. 371-397 ◽  
Author(s):  
Tianyi Wu ◽  
Yuguo Chen ◽  
Jiawei Han
Keyword(s):  
Pattern Mining ◽  
Unified Framework ◽  
Interestingness Measures

scholarly journals A FRAMEWORK FOR INCREMENTAL PARALLEL MINING OF INTERESTING ASSOCIATION PATTERNS FOR BIG DATA

2020 ◽  
pp. 106-117
Author(s):  
Ahmed Sultan Alhegami ◽  
Hussein Alkhader Alsaeedi
Keyword(s):  
Big Data ◽  
Association Rule ◽  
Association Rule Mining ◽  
Domain Knowledge ◽  
Big Data Analysis ◽  
Distributed Environment ◽  
Rule Mining ◽  
Interestingness Measures ◽  
Parallel Mining ◽  
Public Datasets

Association rule mining plays a very important role in the distributed environment for Big Data analysis. The massive volume of data creates imminent needs to design novel, parallel and incremental algorithms for the association rule mining in order to handle Big Data. In this paper, a framework is proposed for incremental parallel interesting association rule mining algorithm for Big Data. The proposed framework incorporates interestingness measures during the process of mining. The proposed framework works to process the incremental data, which usually comes at different times, the user's important knowledge is explored by processing of new data only, without having to return from scratch. One of the main features of this framework is to consider the user domain knowledge, which is monotonically increased. The model that incorporates the users’ belief during the extraction of patterns is attractive, effective and efficient. The proposed framework is implemented on public datasets as well as it is evaluated based on the interesting results that are found.

Learning Different Concept Hierarchies and the Relations between them from Classified Data

Data Mining ◽  
2013 ◽  
pp. 125-141
Author(s):  
Fernando Benites ◽  
Elena Sapozhnikova
Keyword(s):  
Data Mining ◽  
Real World ◽  
Rule Mining ◽  
Mining System ◽  
Interestingness Measures ◽  
Class Hierarchies ◽  
Data Mining System ◽  
Real World Datasets ◽  
Class Labels ◽  
Concept Hierarchies

Methods for the automatic extraction of taxonomies and concept hierarchies from data have recently emerged as essential assistance for humans in ontology construction. The objective of this chapter is to show how the extraction of concept hierarchies and finding relations between them can be effectively coupled with a multi-label classification task. The authors introduce a data mining system which performs classification and addresses both issues by means of association rule mining. The proposed system has been tested on two real-world datasets with the class labels of each dataset coming from two different class hierarchies. Several experiments on hierarchy extraction and concept relation were conducted in order to evaluate the system and three different interestingness measures were applied, to select the most important relations between concepts. One of the measures was developed by the authors. The experimental results showed that the system is able to infer quite accurate concept hierarchies and associations among the concepts. It is therefore well suited for classification-based reasoning.

Learning Different Concept Hierarchies and the Relations Between them from Classified Data

2012 ◽  
pp. 18-34 ◽  
Author(s):  
Fernando Benites ◽  
Elena Sapozhnikova
Keyword(s):  
Data Mining ◽  
Real World ◽  
Rule Mining ◽  
Mining System ◽  
Interestingness Measures ◽  
Class Hierarchies ◽  
Data Mining System ◽  
Real World Datasets ◽  
Class Labels ◽  
Concept Hierarchies

Methods for the automatic extraction of taxonomies and concept hierarchies from data have recently emerged as essential assistance for humans in ontology construction. The objective of this chapter is to show how the extraction of concept hierarchies and finding relations between them can be effectively coupled with a multi-label classification task. The authors introduce a data mining system which performs classification and addresses both issues by means of association rule mining. The proposed system has been tested on two real-world datasets with the class labels of each dataset coming from two different class hierarchies. Several experiments on hierarchy extraction and concept relation were conducted in order to evaluate the system and three different interestingness measures were applied, to select the most important relations between concepts. One of the measures was developed by the authors. The experimental results showed that the system is able to infer quite accurate concept hierarchies and associations among the concepts. It is therefore well suited for classification-based reasoning.

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