scholarly journals Aggregating Interestingness Measures in Associative Classifiers

2018 ◽  
Author(s):  
Matheus Freitas Da Silva ◽  
Veronica Oliveira De Carvalho
Keyword(s):  
Interestingness Measures ◽  
Associative Classifiers

A classificação associativa, a qual vem sendo muito utilizada em diversos domínios, visa a obtenção de um modelo preditivo em que o processo é baseado na extração de regras de associação. A geração do modelo ocorre em etapas, sendo uma delas voltadas a ordenar e podar um conjunto de regras. No que se refere a ordenação, uma das soluções é ranquear as regras por meio de medidas objetivas (MOs). O critério de ordenação impacta a acurácia do classificador. Nos trabalhos da literatura as MOs são exploradas individualmente. Diante do exposto, este trabalho tem por objetivo explorar a agregação de medidas, em que várias MOs são consideradas ao mesmo tempo, no contexto de classificadores associativos.

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.

Mining interestingness measures for string pattern mining

2012 ◽  
Vol 25 (1) ◽  
pp. 45-50 ◽  
Author(s):  
M. Baena-Garcı´a ◽  
R. Morales-Bueno
Keyword(s):  
Pattern Mining ◽  
Interestingness Measures ◽  
String Pattern

scholarly journals A review of associative classification mining

2007 ◽  
Vol 22 (1) ◽  
pp. 37-65 ◽  
Author(s):  
FADI THABTAH
Keyword(s):  
Classification Systems ◽  
Quality Data ◽  
Data Sets ◽  
Rule Discovery ◽  
Associative Classification ◽  
Future Directions ◽  
Association Rule Discovery ◽  
Rule Pruning ◽  
Associative Classifiers ◽  
Pruning Rule

AbstractAssociative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper.

scholarly journals A novel interestingness measure based on fusion model for association rules mining

2021 ◽  
Vol 336 ◽  
pp. 05009
Author(s):  
Junrui Yang ◽  
Lin Xu
Keyword(s):  
Association Rules ◽  
Negative Association ◽  
Fusion Model ◽  
Interestingness Measures ◽  
Association Rules Mining ◽  
Interestingness Measure ◽  
Advantages And Disadvantages ◽  
Measure Model ◽  
Negative Association Rules ◽  
The Difference

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.

scholarly journals An Efficient Method for Associative Classification using Jaccard Measure

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3448-3453
Keyword(s):  
Association Rules ◽  
Vital Role ◽  
Classification Methods ◽  
Associative Classification ◽  
Data Mining Technique ◽  
Target Class ◽  
Interestingness Measures ◽  
Mining Technique ◽  
Major Disadvantage ◽  
Traditional Classification

Classification is a data mining technique that categorizes the items in a database to target classes. The aim of classification is to accurately find the target class for each instance of the data. Associative classification is a classification method that uses Class Association Rules for classification. Associative classification is found to be often more accurate than some traditional classification methods. The major disadvantage of associative classification is the generation of redundant and weak class association rules. Weak class association rules results in increase in size and decrease in accuracy of the classifier. This paper proposes an efficient approach to build a compact and accurate classifier by using interestingness measures for pruning rules. Interestingness measures play a vital role in reducing the size and increasing the accuracy of classifier by pruning redundant or weak rules. Rules which are strong are retained and these rules are further used to build the classifier. The source of the data used in this paper is University of California Irvine Machine Learning Repository. The approach proposed in this paper is effective and the results show that the approach can produce a highly compact and accurate classifier

scholarly journals Conceptual Coverage Driven by Essential Concepts: A Formal Concept Analysis Approach

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2694
Author(s):  
Amira Mouakher ◽  
Axel Ragobert ◽  
Sébastien Gerin ◽  
Andrea Ko
Keyword(s):  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Order Structure ◽  
Formal Concept ◽  
Massive Datasets ◽  
Interestingness Measures ◽  
Galois Lattice ◽  
Formal Concepts ◽  
Representation Method

Formal concept analysis (FCA) is a mathematical theory that is typically used as a knowledge representation method. The approach starts with an input binary relation specifying a set of objects and attributes, finds the natural groupings (formal concepts) described in the data, and then organizes the concepts in a partial order structure or concept (Galois) lattice. Unfortunately, the total number of concepts in this structure tends to grow exponentially as the size of the data increases. Therefore, there are numerous approaches for selecting a subset of concepts to provide full or partial coverage. In this paper, we rely on the battery of mathematical models offered by FCA to introduce a new greedy algorithm, called Concise, to compute minimal and meaningful subsets of concepts. Thanks to its theoretical properties, the Concise algorithm is shown to avoid the sluggishness of its competitors while offering the ability to mine both partial and full conceptual coverage of formal contexts. Furthermore, experiments on massive datasets also underscore the preservation of the quality of the mined formal concepts through interestingness measures agreed upon by the community.

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.  

Sign in / Sign up

Export Citation Format

Share Document