Fuzzy data mining for discovering changes in association rules over time

2003 ◽  
Author(s):  
Wai-Ho Au ◽  
K.C.C. Chan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Fuzzy Data ◽  
Over Time

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.

Mining Generalized Association Rules in an Evolving Environment

2011 ◽  
pp. 1268-1274
Author(s):  
Wen-Yang Lin ◽  
Ming-Cheng Tseng
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
State Of The Art ◽  
Dynamic Environment ◽  
Static Data ◽  
Generalized Association Rules ◽  
Static Item ◽  
Data Source ◽  
Over Time ◽  
Art Techniques

The mining of Generalized Association Rules (GARs) from a large transactional database in the presence of item taxonomy has been recognized as an important model for data mining. Most previous studies on mining generalized association rules, however, were conducted on the assumption of a static environment, i.e., static data source and static item taxonomy, disregarding the fact that the taxonomy might be updated as new transactions are added into the database over time, and as such, the analysts may have to continuously change the support and confidence constraints, or to adjust the taxonomies from different viewpoints to discover more informative rules. In this chapter, we consider the problem of mining generalized association rules in such a dynamic environment. We survey different strategies incorporating state-of-the-art techniques for dealing with this problem and investigate how to efficiently update the discovered association rules when there are transaction updates to the database along with item taxonomy evolution and refinement of support constraint.

Fuzzy data mining for interesting generalized association rules

2003 ◽  
Vol 138 (2) ◽  
pp. 255-269 ◽  
Author(s):  
Tzung-Pei Hong ◽  
Kuei-Ying Lin ◽  
Shyue-Liang Wang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Fuzzy Data ◽  
Generalized Association Rules

Mining Rare Association Rules by Discovering Quasi-Functional Dependencies

2010 ◽  
pp. 131-149
Author(s):  
Giulia Bruno ◽  
Paolo Garza ◽  
Elisa Quintarelli
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Incremental Algorithm ◽  
Functional Dependencies ◽  
Data Mining Technique ◽  
Data Set ◽  
Rare Association ◽  
Normal Behavior ◽  
The Impact ◽  
Over Time

In the context of anomaly detection, the data mining technique of extracting association rules can be used to identify rare rules which represent infrequent situations. A method to detect rare rules is to first infer the normal behavior of objects in the form of quasi-functional dependencies (i.e. functional dependencies that frequently hold), and then analyzing rare violations with respect to them. The quasi-functional dependencies are usually inferred from the current instance of a database. However, in several applications, the database is not static, but new data are added or deleted continuously. Thus, the anomalies have to be updated because they change over time. In this chapter, we propose an incremental algorithm to efficiently maintain up-to-date rules (i.e., functional and quasi-functional dependencies). The impact of the cardinality of the data set and the number of new tuples on the execution time is evaluated through a set of experiments on synthetic and real databases, whose results are here reported.

How Useful Can Be Data Mining For A Continuos Speech Therapist’s Education?

2014 ◽  
Vol 1 (1) ◽  
pp. 339-342
Author(s):  
Mirela Danubianu ◽  
Dragos Mircea Danubianu
Keyword(s):  
Data Mining ◽  
Information And Communication Technology ◽  
Association Rules ◽  
Communication Technology ◽  
Speech Therapy ◽  
Proper Treatment ◽  
Speech Impairments ◽  
Information And Communication ◽  
Specific Education

AbstractSpeech therapy can be viewed as a business in logopaedic area that aims to offer services for correcting language. A proper treatment of speech impairments ensures improved efficiency of therapy, so, in order to do that, a therapist must continuously learn how to adjust its therapy methods to patient's characteristics. Using Information and Communication Technology in this area allowed collecting a lot of data regarding various aspects of treatment. These data can be used for a data mining process in order to find useful and usable patterns and models which help therapists to improve its specific education. Clustering, classification or association rules can provide unexpected information which help to complete therapist's knowledge and to adapt the therapy to patient's needs.

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

2011 ◽  
Vol 145 ◽  
pp. 292-296
Author(s):  
Lee Wen Huang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Large Databases ◽  
Closed Itemsets ◽  
Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

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