An Investigation of Objective Interestingness Measures for Association Rule Mining

2016 ◽  
pp. 472-481 ◽  
Author(s):  
Ratchasak Somyanonthanakul ◽  
Thanaruk Theeramunkong
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
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

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.

scholarly journals Better Rulesets by Removing Redundant Specialisations and Generalisations in Association Rule Mining

2017 ◽  
Vol 21 ◽  
Author(s):  
Henry Petersen ◽  
Josiah Poon ◽  
Simon Poon ◽  
Clement Loy
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Synthetic Data ◽  
Rule Mining ◽  
Interestingness Measures ◽  
Average Rule ◽  
Complex Interactions ◽  
Rule Sets ◽  
Definition Of ◽  
Associative Classifiers

Association rule mining is a fundamental task in many data mining and analysis applications, both for knowledge extraction and as part of other processes (for example, building associative classifiers). It is well known that the number of associations identified by many association rule mining algorithms can be so large as to present a barrier to their interpretability and practical use. A typical solution to this problem involves removing redundant rules. This paper proposes a novel definition of redundancy, which is used to identify only the most interesting associations. Compared to existing redundancy based approaches, our method is both more robust to noise, and produces fewer overall rules for a given data (improving clarity). A rule can be considered redundant if the knowledge it describes is already contained in other rules. Given an association rule, most existing approaches consider rules to be redundant if they add additional variables without increasing quality according to some measure of interestingness. We claim that complex interactions between variables can confound many interestingness measures. This can lead to existing approaches being overly aggressive in removing redundant associations. Most existing approaches also fail to take into account situations where more general rules (those with fewer attributes) can be considered redundant with respect to their specialisations. We examine this problem and provide concrete examples of such errors using artificial data. An alternate definition of redundancy that addresses these issues is proposed. Our approach is shown to identify interesting associations missed by comparable methods on multiple real and synthetic data. When combined with the removal of redundant generalisations, our approach is often able to generate smaller overall rule sets, while leaving average rule quality unaffected or slightly improved.

A Kernel Density Estimation Based Interestingness Measure for Association Rule Mining

2010 ◽  
Vol 20-23 ◽  
pp. 389-394
Author(s):  
Zhi Feng Hao ◽  
Rui Chu Cai ◽  
Tang Wu ◽  
Yi Yuan Zhou
Keyword(s):  
Density Estimation ◽  
Association Rules ◽  
Kernel Density Estimation ◽  
Association Rule ◽  
Association Rule Mining ◽  
Kernel Density ◽  
Rule Mining ◽  
Data Set ◽  
Interestingness Measures ◽  
Interestingness Measure

Association rules provide a concise statement of potentially useful information, and have been widely used in real applications. However, the usefulness of association rules highly depends on the interestingness measure which is used to select interesting rules from millions of candidates. In this study, a probability analysis of association rules is conducted, and a discrete kernel density estimation based interestingness measure is proposed accordingly. The new proposed interestingness measure makes the most of the information contained in the data set and obtains much lower falsely discovery rate than the existing interestingness measures. Experimental results show the effectiveness of the proposed interestingness measure.

scholarly journals Frequent Closed Itemsets Based Condensed Representations for Association Rules

2009 ◽  
pp. 246-271 ◽  
Author(s):  
Nicolas Pasquier
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Theoretical Frameworks ◽  
Interestingness Measures ◽  
Rule Structure ◽  
Closed Itemsets ◽  
Condensed Representations ◽  
Frequent Closed Itemset

After more than one decade of researches on association rule mining, efficient and scalable techniques for the discovery of relevant association rules from large high-dimensional datasets are now available. Most initial studies have focused on the development of theoretical frameworks and efficient algorithms and data structures for association rule mining. However, many applications of association rules to data from different domains have shown that techniques for filtering irrelevant and useless association rules are required to simplify their interpretation by the end-user. Solutions proposed to address this problem can be classified in four main trends: constraint-based mining, interestingness measures, association rule structure analysis, and condensed representations. This chapter focuses on condensed representations that are characterized in the frequent closed itemset framework to expose their advantages and drawbacks.

Sign in / Sign up

Export Citation Format

Share Document