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An improved evaluation methodology for mining association rules.

1. Introduction

2. a review of relevant research on association rules, 2.1. algorithm of association rules, 2.2. application of association rules, 2.3. evaluation method of association rules, 2.4. evaluation method and framework of association rules, 3. review on interestingness measure indicators for association rules, 3.1. support and confidence, 3.3. validity, 3.4. conviction, 3.5. improvement, 3.6. chi-square analysis, 4. the improvement of objective interestingness measures, 4.1. bi-support, 4.2. bi-lift, 4.3. bi-improvement, 4.4. bi-confidence, 5. the bi-directional measure framework of association rules and experimental analysis, 5.1. numerical analysis of simulated data sets.

Pseudocode of the proposed measure framework.

Shopping lists
High value association rules
Calculate frequent 1-itemsets L .
Find frequent 2-itemsets L with L : l ⋈ l , namely );
Calculate the Bi-support of the association rules:
(1) Supp Support threshold (min Supp.)
(2) Supp( Support threshold (min Supp.)
Calculate the Confidence of the association rules, namely Conf ):
Conf Confidence threshold (min Conf.)
Calculate the Bi-confidence of the association rules, namely Bi-conf ):
Bi-conf Bi-confidence threshold (min Bi-conf.)
Output the high value association rules.

5.2. Verification Analysis of Public Data Sets

6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Click here to enlarge figure

Tid	E	F	G	H	I	J	K	L	M	N	R	Total
1	1	1	1	1	1	1	1	0	1	0	0	8
2	1	1	0	1	1	1	1	1	1	0	0	8
3	1	1	0	1	1	0	0	0	0	1	0	5
4	1	1	1	0	0	1	0	1	1	1	1	8
5	1	1	1	0	0	1	0	0	0	1	1	6
6	1	1	1	0	0	0	0	0	1	1	1	6
7	1	1	0	0	0	0	1	0	0	1	0	4
8	1	1	1	0	1	0	0	0	0	0	1	5
9	1	0	1	1	0	0	0	0	0	1	0	4
10	1	0	1	0	0	1	0	0	0	0	1	4
Total	10	8	7	4	4	5	3	2	4	6	5	58

	Occur	Not Occur	Total
occur	50	30	80
not occur	20	0	20
Total	70	30	100

			Total
occur	825	75	900
not occur	50	50	100
Total	875	125	1000

	D Occur	D Not Occur	Total
C occur	345	55	400
C not occur	385	215	600
Total	730	270	1000

Rules	Supp.	Conf.	Lift	Val.	Conv.	Imp.	Csa.	Bi-lift	Bi-imp.	Bi-conf.
M→J	0.3	0.75	1.5	0.1	2	0.25	1.58	2.25	0.16	0.42
M→G	0.3	0.75	1.08	0.1	1.2	0.05	0.35	1.13	0.03	0.08
J→G	0.4	0.8	1.14	0.1	0.75	0.1	0.69	1.33	0.1	0.2
I→H	0.3	0.75	1.88	0.2	2.4	0.35	2.26	4.5	0.23	0.58
I→F	0.4	1	1.25	0	/	0.2	1.58	1.5	0.13	0.33
H→F	0.3	0.75	0.95	−0.2	0.8	−0.05	−0.4	0.9	−0.03	−0.08
R→G	0.5	1	1.42	0.3	/	0.3	2.1	2.5	0.3	0.6
N→G	0.4	0.67	0.95	0.1	0.9	−0.03	−0.21	0.89	−0.05	−0.08
R→F	0.4	0.8	1	0	1	0	0	1	0	0
N→F	0.5	0.83	1.04	0.2	1.2	0.03	0.24	1.11	0.05	0.08
M→F	0.4	1	1.25	0	/	0.2	1.58	1.5	0.13	0.33
J→F	0.4	0.8	1	0	1	0	0	1	0	0
G→F	0.5	0.71	0.89	0.2	0.7	−0.09	−0.71	0.71	−0.21	−0.28
J→M	0.3	0.6	1.5	0.2	1.5	0.2	1.29	3	0.2	0.4
G→J	0.4	0.57	1.14	0.3	1.17	0.07	0.44	1.71	0.18	0.24
H→I	0.3	0.75	1.88	0.2	2.4	0.35	2.26	4.5	0.23	0.58
F→I	0.4	0.5	1.25	0.4	1.2	0.1	0.65	/	0.4	0.5
G→R	0.5	0.71	1.42	0.5	1.75	0.21	1.33	/	0.49	0.71
G→N	0.4	0.57	0.95	0.2	0.93	−0.03	−0.19	0.86	−0.07	−0.1
F→R	0.4	0.5	1	0.3	1	0	0	1	0	0
F→N	0.5	0.63	1.04	0.4	1.07	0.03	0.19	1.25	0.12	0.13
F→M	0.4	0.5	1.25	0.4	1.2	0.1	0.65	/	0.4	0.5
F→J	0.4	0.5	1	0.3	1	0	0	1	0	0
F→G	0.5	0.63	0.89	0.3	0.8	−0.07	−0.48	0.63	−0.28	−0.38
M→L	0.2	0.5	2.5	0.2	0.64	0.3	2.37	/	0.2	0.5

Rules	Supp. AB	Supp.	Conf.	Lift	Imp.	Csa.	Bi-lift	Bi-Imp.	Bi-conf.
F→I	0.4	0.2	0.50	1.25	0.10	0.65	/	0.40	0.50
F→K	0.3	0.2	0.38	1.25	0.08	0.52	/	0.30	0.38
F→L	0.2	0.2	0.25	1.25	0.05	0.40	/	0.20	0.25
F→M	0.4	0.2	0.50	1.25	0.10	0.65	/	0.40	0.50
G→J	0.4	0.2	0.57	1.14	0.07	0.45	1.71	0.17	0.24
G→R	0.5	0.3	0.71	1.43	0.21	1.36	/	0.50	0.71
H→I	0.3	0.5	0.75	1.88	0.35	2.26	4.50	0.23	0.58
H→K	0.2	0.5	0.50	1.67	0.20	1.38	3.00	0.13	0.33
I→K	0.2	0.5	0.50	1.67	0.20	1.38	3.00	0.13	0.33
J→K	0.2	0.4	0.40	1.33	0.10	0.69	2.00	0.10	0.20
J→L	0.2	0.5	0.40	2.00	0.20	1.58	/	0.20	0.40
J→M	0.3	0.4	0.60	1.50	0.20	1.29	3.00	0.20	0.40
J→R	0.3	0.3	0.60	1.20	0.10	0.63	1.50	0.10	0.20
K→M	0.2	0.5	0.67	1.67	0.27	1.72	2.33	0.11	0.38
L→M	0.2	0.6	1.00	2.50	0.60	3.87	4.00	0.15	0.75
I→F	0.4	0.2	1.00	1.25	0.20	1.58	1.50	0.13	0.33
K→F	0.3	0.2	1.00	1.25	0.20	1.58	1.40	0.09	0.29
L→F	0.2	0.2	1.00	1.25	0.20	1.58	1.33	0.05	0.25
M→F	0.4	0.2	1.00	1.25	0.20	1.58	1.50	0.13	0.33
J→G	0.4	0.2	0.80	1.14	0.10	0.69	1.33	0.10	0.20
R→G	0.5	0.3	1.00	1.43	0.30	2.07	2.50	0.30	0.60
I→H	0.3	0.5	0.75	1.88	0.35	2.26	4.50	0.23	0.58
K→H	0.2	0.5	0.67	1.67	0.27	1.72	2.33	0.11	0.38
K→I	0.2	0.5	0.67	1.67	0.27	1.72	2.33	0.11	0.38
K→J	0.2	0.4	0.67	1.33	0.17	1.05	1.56	0.07	0.24
L→J	0.2	0.5	1.00	2.00	0.50	3.16	2.67	0.13	0.63
M→J	0.3	0.4	0.75	1.50	0.25	1.58	2.25	0.17	0.42
R→J	0.3	0.3	0.60	1.20	0.10	0.63	1.50	0.10	0.20
M→K	0.2	0.5	0.50	1.67	0.20	1.38	3.00	0.13	0.33
M→L	0.2	0.6	0.50	2.50	0.30	2.37	/	0.20	0.50

Framework	Conditions	High Value	Low Value	No Value	Total
support-confidence	min supp. (AB) = 0.2, and min conf. (AB) = 0.5	9	7	9	25
support-confidence	min supp. (AB) = 0.2, and min conf. (AB) = 0.2	12	22	32	66
Bi-support and Bi-confidence	min supp. (AB) = 0.2, min supp. ) = 0.2, min conf. (AB) = 0.2, and Bi-conf. (AB) = 0.2	12	18	0	30

Rules	Supp. AB	Supp.	Conf.	Lift	Imp.	Csa.	Bi-lift	Bi-Imp.	Bi-conf.
L→M	0.2	0.6	1.00	2.50	0.60	3.87	4.00	0.15	0.75
G→R	0.5	0.3	0.71	1.43	0.21	1.36		0.50	0.71
L→J	0.2	0.5	1.00	2.00	0.50	3.16	2.67	0.13	0.63
R→G	0.5	0.3	1.00	1.43	0.30	2.07	2.50	0.30	0.60
H→I	0.3	0.5	0.75	1.88	0.35	2.26	4.50	0.23	0.58
I→H	0.3	0.5	0.75	1.88	0.35	2.26	4.50	0.23	0.58
M→L	0.2	0.6	0.50	2.50	0.30	2.37		0.20	0.50
F→I	0.4	0.2	0.50	1.25	0.10	0.65		0.40	0.50
F→M	0.4	0.2	0.50	1.25	0.10	0.65		0.40	0.50
M→J	0.3	0.4	0.75	1.50	0.25	1.58	2.25	0.17	0.42
J→L	0.2	0.5	0.40	2.00	0.20	1.58		0.20	0.40
J→M	0.3	0.4	0.60	1.50	0.20	1.29	3.00		0.40

Rules	Supp.	Conf.	Lift	Imp.	Csa.	Bi-lift	Bi-imp.	Bi-conf.
G→R	0.5	0.71	1.42	0.21	1.33	/	0.49	0.71
R→G	0.5	1	1.42	0.3	2.1	2.5	0.3	0.6
I→H	0.3	0.75	1.88	0.35	2.26	4.5	0.23	0.58
H→I	0.3	0.75	1.88	0.35	2.26	4.5	0.23	0.58
F→I	0.4	0.5	1.25	0.1	0.65	/	0.4	0.5
F→M	0.4	0.5	1.25	0.1	0.65	/	0.4	0.5
M→L	0.2	0.5	2.5	0.3	2.37	/	0.2	0.5
M→J	0.3	0.75	1.5	0.25	1.58	2.25	0.16	0.42
J→M	0.3	0.6	1.5	0.2	1.29	3	0.2	0.4

Framework	Conditions	High Value	Low Value	No Value	Total
support-confidence	min supp. (AB) = 0.05, and min conf. (AB) = 0.2	10	14	48	72
Bi-support and Bi-confidence		10	5	0	15

Rules	Supp. AB		Conf.	Bi-conf.
Beer→Frozen meat	0.29	0.57	0.58	0.39
Frozen meat→beer	0.30	0.57	0.56	0.39
Frozen meat→Canned vegetables	0.30	0.56	0.57	0.39
Canned vegetables→Frozen meat	0.30	0.56	0.57	0.39
Beer→Canned vegetables	0.29	0.57	0.57	0.38
Canned vegetables→beer	0.30	0.57	0.55	0.37
Candy→wine	0.27	0.58	0.52	0.32
Wine→candy	0.28	0.58	0.50	0.32
Fish→Fruits and vegetables	0.29	0.55	0.50	0.28
Fruits and vegetables→fish	0.29	0.55	0.48	0.28
Beer→Frozen meat	0.29	0.57	0.58	0.39
Frozen meat→beer	0.30	0.57	0.56	0.39

Framework	Conditions	High Value	Low Value	No Value	Total
support-confidence	min supp. (AB) = 0.02, and min conf. (AB) = 0.1	59	37	18	114
Bi-support and Bi-confidence	min supp. (AB) = 0.02 min supp. ) = 0.02, min conf. (AB) = 0.1 and Bi-conf. (AB) = 0.1	50	7	0	57

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Bao, F.; Mao, L.; Zhu, Y.; Xiao, C.; Xu, C. An Improved Evaluation Methodology for Mining Association Rules. Axioms 2022 , 11 , 17. https://doi.org/10.3390/axioms11010017

Bao F, Mao L, Zhu Y, Xiao C, Xu C. An Improved Evaluation Methodology for Mining Association Rules. Axioms . 2022; 11(1):17. https://doi.org/10.3390/axioms11010017

Bao, Fuguang, Linghao Mao, Yiling Zhu, Cancan Xiao, and Chonghuan Xu. 2022. "An Improved Evaluation Methodology for Mining Association Rules" Axioms 11, no. 1: 17. https://doi.org/10.3390/axioms11010017

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Corpus ID: 671244

Association Rules Mining: A Recent Overview

S. Kotsiantis , D. Kanellopoulos
Published 2006
Computer Science, Mathematics

625 Citations

Algorithms for association rule mining:a general survey on benefits and drawbacks of algorithms, an exhaustive study on association rule mining, association rule mining : a review, mining of association rule-a review paper, association rule mining: a survey, association rule mining: a review, a study on milestones of association rule mining algorithms in large databases, a study on effective mining of association rules from huge databases, a study of different association rule mining techniques, association rule mining: an overview, 41 references, mining for strong negative associations in a large database of customer transactions, prices: an efficient algorithm for mining association rules, a new approach of eliminating redundant association rules, algorithms for association rules, mining association rules between sets of items in large databases, mining negative association rules, fast algorithms for mining association rules, mining frequent itemsets with category-based constraints, pruning redundant association rules using maximum entropy principle, redundant association rules reduction techniques, related papers.

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Mining association rules based on apriori algorithm and application.

In the data mining research, mining association rules is an important topic. Apriori algorithm submitted by Agrawal and R. Srikant in 1994 is the most effective algorithm. Aimed at two problems of discovering frequent itemsets in a large database and ...

Research and improvement on association rule algorithm based on FP-Growth

Association rules mining (ARM) is one of the most useful techniques in the field of knowledge discovery and data mining and so on. Frequent item sets mining plays an important role in association rules mining. Apriori algorithm and FP-growth algorithm ...

Association rule mining and quantitative association rule mining among infrequent items

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Research: How Should Multinational Firms Navigate Local Rules?

Patrick Regnér
Ivar Padrón-Hernández

A study of five global companies identified tactics to maintain a competitive edge.

When multinational firms enter new markets, they have to choose how to manage the formal and informal local rules that can vary greatly. Local institutions and rules matter, and there are clear risks for top executives at headquarters who don’t take them seriously. But constantly adapting to local practices could jeopardize a company’s ability to integrate operations across markets. A study of five globally successful Scandinavian companies identified six tactics that firms can use to address local rules while maintaining competitive advantage: Avoid, alter, adapt, imitate, influence, and innovate.

Multinational companies seeking growth opportunities often find them when they enter new countries. Previous research has emphasized the liabilities of foreignness, i.e., the many disadvantages foreign companies face as they do business in new markets. To overcome these liabilities, corporations are often recommended to adapt to local rules. However, as they adapt their strategies and practices to local ones, they risk losing the very basis for their internationalization and advantage over local competitors: the integration of operations across markets.

Patrick Regnér is professor of strategic management at Stockholm School of Economics and the director of the Center for Strategy and Competitiveness. His current research centers on how diverse types of managerial activities and practices shape strategy development and creation in distinct ways. Another research stream focuses on how multinational corporations can respond strategically to institutions.
Ivar Padrón-Hernández is an assistant professor at the Institute of Innovation Research at Hitotsubashi University. His research focuses on how firms change societies as they introduce innovative products, services and ideas across national borders.

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Summary of Association Rules

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(PDF) A SURVEY OF ASSOCIATION RULES
(PDF) Classification approach based on association rules mining for
(PDF) A Method Based on Association Rules to Construct Product Line Models
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A survey on the use of association rules mining techniques in textual
To this end, the paper uses association rules and other techniques such as clustering. ... The research reported in this paper was partially supported by the COPKIT project under the European Union's Horizon 2020 research and innovation program (grant agreement No 786687), the Andalusian government and the FEDER operative program under the ...
A comprehensive review of visualization methods for association rule
The selection criteria were the follows: (1) research paper addresses any kind of ARM and its connection with visualization, and the research must be peer reviewed, i.e., published in a referred conference, journal paper, book chapter or monograph. ... New ideas in the visualization of association rules. This section reviews papers dealing with ...
Horizontal Learning Approach to Discover Association Rules
Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of discovering the association rules. Most of the literature pertaining to the association rules deals extensively ...
(PDF) Research on Association Rule Mining
Abstract. Association Rule Mining is one of the important areas of research, receiving increasing attention. It is an essential part of Knowledge Discovery in Databases (KDD). The scope of ...
An Improved Evaluation Methodology for Mining Association Rules
At present, association rules have been widely used in prediction, personalized recommendation, risk analysis and other fields. However, it has been pointed out that the traditional framework to evaluate association rules, based on Support and Confidence as measures of importance and accuracy, has several drawbacks. Some papers presented several new evaluation methods; the most typical methods ...
A Comparative Analysis of Association Rule Mining Algorithms
The paper also provides a minor distinction focused on the results of various algorithms related to association rules mining. The paper provides a short description of the principles and ...
Fundamentals of association rules in data mining and knowledge
Association rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets in datasets and predicts the associative and correlative behaviors for new data.
An Improved Apriori Algorithm For Association Rules
In this paper we present new scheme for extracting association rules that considers the time, number of database scans, memory consumption, and the interestingness of the rules.
Association Rules Mining: A Recent Overview
The preliminaries of basic concepts about association rule mining are provided and the list of existing association rulemining techniques are surveyed. In this paper, we provide the preliminaries of basic concepts about association rule mining and survey the list of existing association rule mining techniques. Of course, a single article cannot be a complete review of all the al- gorithms, yet ...
Efficient Analysis of Pattern and Association Rule Mining Approaches
fields (hierarchical association rules [8]). This paper is organized as follows: in Section 2, we briefly describe association rules mining. Section 3 summarizes kinds of frequent pattern mining and association rule mining. Section 4 details a review of association rules approaches. In Section 5, we describe
PDF A Survey of Association Rules
The objective of this paper is to provide a thorough survey of previous research on association rules. In the next section we give a formal definition of association rules. Section 3 contains the description of sequential and parallel algorithms as well as other algorithms to find association rules.
Research on Association Rule Mining
The aim of association rule mining is to find interesting and useful patterns in a transaction database. Each transaction in the database contains a set of items and a transaction identifier (e.g., a market basket). Association rules are rules of the form X → Y where X and Y are two disjoint subsets of all available items.
Application research of association rule based on Apriori algorithm
In the data mining research, mining association rules is an important topic. Apriori algorithm submitted by Agrawal and R. Srikant in 1994 is the most effective algorithm. ... Apriori algorithm and fp-growth are both classical algorithms of association rules. This paper collects the employment situation of graduates of a college of Electrical ...
A Survey on Association Rule Mining for Enterprise ...
Association Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predictive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suitable for ...
PDF An introduction to association rule mining: An application in
growth in the number of association rules as the number of variables used increases. In ARM, two measures are com-monly used to help a researcher decide the usefulness of an association rule: support and confidence. The support of an association rule A 2 B is the percentage of transactions that contain A Ø B. The confidence of an association rule
Research of Commonly Used Association Rules Mining Algorithm in Data
This paper introduces the concept of data mining and its an important branch - association rules, describes the basic concept of association rules, the basic model of mining association rules, introduces the classical algorithm of association rules, and then classified discusses the association rules mining from several angles such as width, depth, partition, sampling and incremental updating ...
Association Rules: Problems, solutions and new applications Abstract
Universidad de Salamanca, Plaza Merced S/N, 37008, Salamanca. e-mail: [email protected]. Abstract. Association rule mining is an important. component of data mining. In the last years a great. number of ...
PDF Grouping Association Rules Using Lift
of research papers (see [11] for an overview). Association rules have the potential to be extremely useful for analysts (especially using visualization) and as input for other applications and models. However, a practical drawback of mining and efﬁciently using association rules is that the set of rules returned by the
Research on an Improved Association Rule Mining Algorithm
Data mining association rules is an important role of data mining because of its wide applicability in market analysis by expressing how tangible products and services relate to each other and how rend to group together. The paper proposed Apriori algorithm of riddling compression. And has carried on the simulation, the result demonstrated the Apriori algorithm of riddling compression can ...
Market Basket Analysis: Identify the Changing Trends of Market Data
This paper discusses the data mining technique i.e. association rule mining and provide a new algorithm which may helpful to examine the customer behaviour and assists in increasing the sales. ... Lower association rules (Outliers) Association Rules Score Assigned A C a->c 18 18 C C 85 Manpreet Kaur and Shivani Kang / Procedia Computer Science ...
PDF A Comparative Analysis of Association Rules Mining Algorithms
Generally, an association rules mining algorithm contains the following steps: The set of candidate k-itemsets is generated by 1-extensions of the large (k -1) itemsets generated in the previous iteration. Supports for the candidate k-itemsets are generated by a pass over the database. Itemsets that do not have the minimum support are discarded ...
Data Mining Techniques in Association Rule : A Review
In this paper we present a three-step visualization method for mining market basket association rules. These steps include discovering frequent itemsets, mining association rules and finally ...
Research: How Should Multinational Firms Navigate Local Rules?
A study of five globally successful Scandinavian companies identified six tactics that firms can use to address local rules while maintaining competitive advantage: Avoid, alter, adapt, imitate ...
(PDF) Summary of Association Rules
1. Summary of Association Rules. Foxiao Zhan 1, a, Xiaolan Zhu 1, b, Lei Zhang 1, 2, *, Xuexi Wang 1, c, Lu Wang 1, d, Chaoyi Liu 1, e. 1 Department of Computer Technology and application Qinghai ...