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

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.  

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

Rare Class Association Rule Mining with Multiple Imbalanced Attributes

2010 ◽  
pp. 66-75 ◽  
Author(s):  
Huaifeng Zhang ◽  
Yanchang Zhao ◽  
Longbing Cao ◽  
Chengqi Zhang ◽  
Hans Bohlscheid
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Uniform Space ◽  
Rule Mining ◽  
Target Class ◽  
Left Hand ◽  
The Social ◽  
Rare Class ◽  
Class Association Rule ◽  
The Right

In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This algorithm is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through a standard algorithm while the rules with imbalanced attributes are mined based on newly defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied in the social security field. Although some attributes are severely imbalanced, rules with a minority of imbalanced attributes have been mined efficiently.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

Time Related Class Association Rule Mining and Its Application to Traffic Prediction

2010 ◽  
Vol 130 (2) ◽  
pp. 289-301 ◽  
Author(s):  
Huiyu Zhou ◽  
Shingo Mabu ◽  
Wei Wei ◽  
Kaoru Shimada ◽  
Kotaro Hirasawa
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Traffic Prediction ◽  
Rule Mining ◽  
Class Association Rule
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