Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 842-845 ◽  
Author(s):  
Jia Mei Guo ◽  
Yin Xiang Pei
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Clustering Algorithm ◽  
Original Data ◽  
Data Set ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Redundant Data ◽  
Fuzzy Partitions ◽  
Rules Extraction

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

Different Types of Association Rules Mining Review

2012 ◽  
Vol 241-244 ◽  
pp. 1589-1592
Author(s):  
Jun Tan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Weighted Association Rules ◽  
Application Systems ◽  
Different Types ◽  
Multi Level

In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.

Uncovering Actionable Knowledge in Corporate Data with Qualified Association Rules

2011 ◽  
Vol 2 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Nenad Jukic ◽  
Svetlozar Nestorov ◽  
Miguel Velasco ◽  
Jami Eddington
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Mining Method ◽  
Data Set ◽  
Association Rules Mining ◽  
Standard Data ◽  
Additional Information ◽  
Day Of The Week ◽  
Mining Methods ◽  
Corporate Actions

Association rules mining is one of the most successfully applied data mining methods in today’s business settings (e.g. Amazon or Netflix recommendations to customers). Qualified association rules mining is an extension of the association rules data mining method, that uncovers previously unknown correlations that only manifest themselves under certain circumstances (e.g. on a particular day of the week), with the goal of improving action results, e.g. turning an underperforming campaign (spread too thin over the entire audience) into a highly targeted campaign that delivers results. Such correlations have not been easily reachable using standard data mining tools so far. This paper describes the method for straightforward discovery of qualified association rules and demonstrates the use of qualified association rules mining on an actual corporate data set. The data set is a subset of a corporate data warehouse for Sam’s Club, a division of Wal-Mart Stores, INC. The experiments described in this paper illustrate how qualified association rules supplement standard association rules data mining methods and provide additional information which can be used to better target corporate actions.

Uncovering Actionable Knowledge in Corporate Data with Qualified Association Rules

2012 ◽  
pp. 210-229
Author(s):  
Nenad Jukic ◽  
Svetlozar Nestorov ◽  
Miguel Velasco ◽  
Jami Eddington
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Association Rules ◽  
Data Set ◽  
Association Rules Mining ◽  
Standard Data ◽  
Additional Information ◽  
Day Of The Week ◽  
Mining Methods ◽  
Corporate Actions

Association rules mining is one of the most successfully applied data mining methods in today’s business settings (e.g. Amazon or Netflix recommendations to customers). Qualified association rules mining is an extension of the association rules data mining method, that uncovers previously unknown correlations that only manifest themselves under certain circumstances (e.g. on a particular day of the week), with the goal of improving action results, e.g. turning an underperforming campaign (spread too thin over the entire audience) into a highly targeted campaign that delivers results. Such correlations have not been easily reachable using standard data mining tools so far. This paper describes the method for straightforward discovery of qualified association rules and demonstrates the use of qualified association rules mining on an actual corporate data set. The data set is a subset of a corporate data warehouse for Sam’s Club, a division of Wal-Mart Stores, INC. The experiments described in this paper illustrate how qualified association rules supplement standard association rules data mining methods and provide additional information which can be used to better target corporate actions.

Applying data mining for online CRM marketing strategy

2018 ◽  
Vol 120 (3) ◽  
pp. 665-675 ◽  
Author(s):  
Wen-Yu Chiang
Keyword(s):  
Data Mining ◽  
Marketing Strategy ◽  
Association Rules ◽  
Clustering Algorithm ◽  
Customer Relationship ◽  
Coffee Shop ◽  
Content Type ◽  
Fuzzy Association Rules ◽  
Data Mining Approach ◽  
Coffee Shops

Purpose The purpose of this paper is to propose a data mining approach for mining valuable markets for online customer relationship management (CRM) marketing strategy. The industry of coffee shops in Taiwan is employed as an empirical case study in this research. Design/methodology/approach Via a proposed data mining approach, the study used fuzzy clustering algorithm and Apriori algorithm to analyze customers for obtaining more marketing and purchasing knowledge of online CRM systems. Findings The research found three hard markets and one fuzzy market. Furthermore, the study discovered two association rules and two fuzzy association rules. Originality/value However, industry of coffee shops has been always a fast-growing and competitive business around the world. Thus, marketing strategy is important for this industry. The results and the proposed data mining approach of this research can be used in the industry of coffee shop or other retailers for their online CRM marketing systems.

Evaluation of sustainability using fuzzy association rules mining

2011 ◽  
Vol 13 (6) ◽  
pp. 809-819 ◽  
Author(s):  
S. Vinodh ◽  
K. Eazhil Selvan ◽  
N. Hari Prakash
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
Fuzzy Association Rules

scholarly journals Privacy Preservation using (L, D) Inference Model Based on Dependency Identification Information Gain

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1170-1173
Keyword(s):  
Data Mining ◽  
Information Gain ◽  
Original Data ◽  
Perturbation Approach ◽  
Sensitive Information ◽  
Functional Dependencies ◽  
Inference Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Original Dataset

The improvement of an information processing and Memory capacity, the vast amount of data is collected for various data analyses purposes. Data mining techniques are used to get knowledgeable information. The process of extraction of data by using data mining techniques the data get discovered publically and this leads to breaches of specific privacy data. Privacypreserving data mining is used to provide to protection of sensitive information from unwanted or unsanctioned disclosure. In this paper, we analysis the problem of discovering similarity checks for functional dependencies from a given dataset such that application of algorithm (l, d) inference with generalization can anonymised the micro data without loss in utility. [8] This work has presented Functional dependency based perturbation approach which hides sensitive information from the user, by applying (l, d) inference model on the dependency attributes based on Information Gain. This approach works on both categorical and numerical attributes. The perturbed data set does not affects the original dataset it maintains the same or very comparable patterns as the original data set. Hence the utility of the application is always high, when compared to other data mining techniques. The accuracy of the original and perturbed datasets is compared and analysed using tools, data mining classification algorithm.

Finding Active Membership Functions for Genetic-Fuzzy Data Mining

2015 ◽  
Vol 14 (06) ◽  
pp. 1215-1242 ◽  
Author(s):  
Chun-Hao Chen ◽  
Tzung-Pei Hong ◽  
Yeong-Chyi Lee ◽  
Vincent S. Tseng
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Fuzzy Data ◽  
Membership Functions ◽  
Linguistic Terms ◽  
Fuzzy Association Rules ◽  
Fitness Functions ◽  
The Given ◽  
Binary Strings ◽  
Active Membership

Since transactions may contain quantitative values, many approaches have been proposed to derive membership functions for mining fuzzy association rules using genetic algorithms (GAs), a process known as genetic-fuzzy data mining. However, existing approaches assume that the number of linguistic terms is predefined. Thus, this study proposes a genetic-fuzzy mining approach for extracting an appropriate number of linguistic terms and their membership functions used in fuzzy data mining for the given items. The proposed algorithm adjusts membership functions using GAs and then uses them to fuzzify the quantitative transactions. Each individual in the population represents a possible set of membership functions for the items and is divided into two parts, control genes (CGs) and parametric genes (PGs). CGs are encoded into binary strings and used to determine whether membership functions are active. Each set of membership functions for an item is encoded as PGs with real-number schema. In addition, seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. After the GA process terminates, a better set of association rules with a suitable set of membership functions is obtained. Experiments are made to show the effectiveness of the proposed approach.

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