scholarly journals Interestingness Measures for Multi-Level Association Rules

2013 ◽  
pp. 47-74
Author(s):  
Gavin Shaw ◽  
Yue Xu ◽  
Shlomo Geva
Keyword(s):  
Association Rules ◽  
Interestingness Measures ◽  
Multi Level

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

DWFIST

2008 ◽  
pp. 3142-3163
Author(s):  
Rodrigo Salvador Monteiro ◽  
Geraldo Zimbrao ◽  
Holger Schwarz ◽  
Bernhard Mitschang ◽  
Jano Moreira de Souza
Keyword(s):  
Data Warehouse ◽  
Association Rules ◽  
Original Data ◽  
Frequent Itemsets ◽  
Upper And Lower Bounds ◽  
Sufficient Information ◽  
Interestingness Measures ◽  
The Core ◽  
Pattern Retrieval ◽  
Frequency Counts

This chapter presents the core of the DWFIST approach, which is concerned with supporting the analysis and exploration of frequent itemsets and derived patterns, e.g., association rules in transactional datasets. The goal of this new approach is to provide: (1) flexible pattern-retrieval capabilities without requiring the original data during the analysis phase; and (2) a standard modeling for data warehouses of frequent itemsets, allowing an easier development and reuse of tools for analysis and exploration of itemset-based patterns. Instead of storing the original datasets, our approach organizes frequent itemsets holding on different partitions of the original transactions in a data warehouse that retains sufficient information for future analysis. A running example for mining calendar-based patterns on data streams is presented. Staging area tasks are discussed and standard conceptual and logical schemas are presented. Properties of this standard modeling allow retrieval of frequent itemsets holding on any set of partitions, along with upper and lower bounds on their frequency counts. Furthermore, precision guarantees for some interestingness measures of association rules are provided as well.

scholarly journals ATURAN REKOMENDASI BARANG MENGGUNAKAN MULTI LEVEL ASSOCIATION RULES MINING (ML-ARM)

Transmisi ◽  
2018 ◽  
Vol 20 (2) ◽  
pp. 49
Author(s):  
Zahra Arwananing Tyas
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
Multi Level ◽  
F Measure

Sistem rekomendasi dapat menghasilkan rekomendasi dengan berbagai cara dan menggunakan berbagai macam metode, salah satunya adalah memanfaatkan tumpukan kasus lama atau tumpukan data transaksi lama yang dapat menghasilkan informasi atau aturan dengan metode Association Rules Mining(ARM). Aturan terbentuk dengan metode multi level ARM dan menghasilkan 5 aturan yang akan dicocokkan dengan masukan pengguna. Saat aturan ditemukan cocok maka consequent dari aturan tersebut akan dijadikan hasil rekomendasi.  Hasil pengujian dari aturan yang terbentuk memiliki nilai akurasi 94,12% dan nilai precision, recall dan F-measure untuk sistem rekomendasi ini pada proses rekomendasi dengan aturan yaitu berturut 0,475; 0,513 dan 0,25.

scholarly journals Toward a Theory of Normalizing Function of Interestingness Measure of Binary Association Rules

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Armand Armand ◽  
André Totohasina ◽  
Daniel Rajaonasy Feno
Keyword(s):  
Association Rules ◽  
Quality Measures ◽  
Measures Of Association ◽  
Interestingness Measures ◽  
Association Rules Mining ◽  
Interestingness Measure ◽  
Normalization Function

Regarding the existence of more than sixty interestingness measures proposed in the literature since 1993 till today in the topics of association rules mining and facing the importance these last one, the research on normalization probabilistic quality measures of association rules has already led to many tangible results to consolidate the various existing measures in the literature. This article recommends a simple way to perform this normalization. In the interest of a unified presentation, the article offers also a new concept of normalization function as an effective tool for resolution of the problem of normalization measures that have already their own normalization functions.

scholarly journals Boolean factors as a means of clustering of interestingness measures of association rules

2013 ◽  
Vol 70 (1-2) ◽  
pp. 151-184 ◽  
Author(s):  
Radim Belohlavek ◽  
Dhouha Grissa ◽  
Sylvie Guillaume ◽  
Engelbert Mephu Nguifo ◽  
Jan Outrata
Keyword(s):  
Association Rules ◽  
Measures Of Association ◽  
Interestingness Measures

Mining Undominated Association Rules Through Interestingness Measures

2014 ◽  
Vol 23 (04) ◽  
pp. 1460011 ◽  
Author(s):  
Slim Bouker ◽  
Rabie Saidi ◽  
Sadok Ben Yahia ◽  
Engelbert Mephu Nguifo
Keyword(s):  
Association Rules ◽  
Threshold Value ◽  
Interestingness Measures ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Efficient Selection ◽  
Selection Of

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. In order to bypass this hamper, an efficient selection of rules has to be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures gave rise to a new problem, namely the heterogeneity of the evaluation results and this created confusion to the decision. In this respect, we propose a novel approach to discover interesting association rules without favoring or excluding any measure by adopting the notion of dominance between association rules. Our approach bypasses the problem of measure heterogeneity and unveils a compromise between their evaluations. Interestingly enough, the proposed approach also avoids another non-trivial problem which is the threshold value specification. Extensive carried out experiments on benchmark datasets show the benefits of the introduced approach.

Sign in / Sign up

Export Citation Format

Share Document