Intelligent Fuzzy Database Management Systems

2011 ◽  
pp. 822-846
Author(s):  
Safìye Turgay
Keyword(s):  
Data Mining ◽  
Relational Database ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Fuzzy Data ◽  
Rule Mining ◽  
Mining System ◽  
Agent Based ◽  
Rule Structure ◽  
Fuzzy Database

In this chapter, an agent-based fuzzy data mining structure was developed to process and evaluate data with an enlargement in the knowledge dimension, and to build a rule structure for the system. Within the developed system, the focus was on the operation feature of the fuzzy data mining structure, which is the same for each agent composing the system. The suggested association rules are derived from a relational database. Future tasks of the system will be estimated when the system performs fuzzy data mining more quickly thanks to the distributed, autonomous, intelligent, and communicative agent structure of the suggested agent-based fuzzy rule mining system. In fuzzy rule mining, the system will primarily examine and group the relational database in databases of the agents with fuzzy logic and then will shape the rule base of the system by applying the fuzzy data mining method to these data.

How to Achieve Fuzzy Relational Databases Managing Fuzzy Data and Metadata

2011 ◽  
pp. 351-380 ◽  
Author(s):  
Mohamed Ali Ben Hassine ◽  
Amel Grissa Touzi ◽  
José Galindo ◽  
Habib Ounelli
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Fuzzy Data ◽  
Imprecise Data ◽  
The Third ◽  
Fuzzy Database ◽  
Existing Data ◽  
Flexible Querying ◽  
Mining Tools ◽  
Total Migration

Fuzzy relational databases have been introduced to deal with uncertain or incomplete information demonstrating the efficiency of processing fuzzy queries. For these reasons, many organizations aim to integrate flexible querying to handle imprecise data or to use fuzzy data mining tools, minimizing the transformation costs. The best solution is to offer a smooth migration towards this technology. This chapter presents a migration approach from relational databases towards fuzzy relational databases. This migration is divided into three strategies. The first one, named “partial migration,” is useful basically to include fuzzy queries in classic databases without changing existing data. It needs some definitions (fuzzy metaknowledge) in order to treat fuzzy queries written in FSQL language (Fuzzy SQL). The second one, named “total migration,” offers in addition to the flexible querying, a real fuzzy database, with the possibility to store imprecise data. This strategy requires a modification of schemas, data, and eventually programs. The third strategy is a mixture of the previous strategies, generally as a temporary step, easier and faster than the total migration.

Research on Employment Data Mining for Higher Vocational Graduates

2014 ◽  
Vol 686 ◽  
pp. 290-294
Author(s):  
Feng Lin
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
College Teaching ◽  
Original Data ◽  
Management Decision ◽  
Rule Mining ◽  
Mining System ◽  
Teaching Management ◽  
Effective Use ◽  
Employment Data

In order to make effective use a large amount of graduate data in colleges and universities that accumulate by teaching management of work, the paper study the data mining for higher vocational graduates database using the data mining technology. Using a variety of data preprocessing methods for the original data, and the paper put forward to mining algorithm based on commonly association rule Apriori algorithm, then according to the actual needs of the design and implementation of association rule mining system, has been beneficial to the employment guidance of college teaching management decision and graduates of the mining results.

Learning Different Concept Hierarchies and the Relations between them from Classified Data

Data Mining ◽  
2013 ◽  
pp. 125-141
Author(s):  
Fernando Benites ◽  
Elena Sapozhnikova
Keyword(s):  
Data Mining ◽  
Real World ◽  
Rule Mining ◽  
Mining System ◽  
Interestingness Measures ◽  
Class Hierarchies ◽  
Data Mining System ◽  
Real World Datasets ◽  
Class Labels ◽  
Concept Hierarchies

Methods for the automatic extraction of taxonomies and concept hierarchies from data have recently emerged as essential assistance for humans in ontology construction. The objective of this chapter is to show how the extraction of concept hierarchies and finding relations between them can be effectively coupled with a multi-label classification task. The authors introduce a data mining system which performs classification and addresses both issues by means of association rule mining. The proposed system has been tested on two real-world datasets with the class labels of each dataset coming from two different class hierarchies. Several experiments on hierarchy extraction and concept relation were conducted in order to evaluate the system and three different interestingness measures were applied, to select the most important relations between concepts. One of the measures was developed by the authors. The experimental results showed that the system is able to infer quite accurate concept hierarchies and associations among the concepts. It is therefore well suited for classification-based reasoning.

Learning Different Concept Hierarchies and the Relations Between them from Classified Data

2012 ◽  
pp. 18-34 ◽  
Author(s):  
Fernando Benites ◽  
Elena Sapozhnikova
Keyword(s):  
Data Mining ◽  
Real World ◽  
Rule Mining ◽  
Mining System ◽  
Interestingness Measures ◽  
Class Hierarchies ◽  
Data Mining System ◽  
Real World Datasets ◽  
Class Labels ◽  
Concept Hierarchies

Methods for the automatic extraction of taxonomies and concept hierarchies from data have recently emerged as essential assistance for humans in ontology construction. The objective of this chapter is to show how the extraction of concept hierarchies and finding relations between them can be effectively coupled with a multi-label classification task. The authors introduce a data mining system which performs classification and addresses both issues by means of association rule mining. The proposed system has been tested on two real-world datasets with the class labels of each dataset coming from two different class hierarchies. Several experiments on hierarchy extraction and concept relation were conducted in order to evaluate the system and three different interestingness measures were applied, to select the most important relations between concepts. One of the measures was developed by the authors. The experimental results showed that the system is able to infer quite accurate concept hierarchies and associations among the concepts. It is therefore well suited for classification-based reasoning.

Mining Fuzzy Association Rules in Quantitative Databases

2012 ◽  
Vol 182-183 ◽  
pp. 2003-2007
Author(s):  
Yi Ming Bai ◽  
Xian Yao Meng ◽  
Xin Jie Han
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Fuzzy Rule ◽  
Real Data ◽  
Rule Base ◽  
Linguistic Terms ◽  
Fuzzy Association Rules ◽  
Rule Number ◽  
Discrete Values ◽  
Definition Of

In this paper, we introduce a novel technique for mining fuzzy association rules in quantitative databases. Unlike other data mining techniques who can only discover association rules in discrete values, the algorithm reveals the relationships among different quantitative values by traversing through the partition grids and produces the corresponding Fuzzy Association Rules. Fuzzy Association Rules employs linguistic terms to represent the revealed regularities and exceptions in quantitative databases. After the fuzzy rule base is built, we utilize the definition of Support Degree in data mining to reduce the rule number and save the useful rules. Throughout this paper, we will use a set of real data from a wine database to demonstrate the ideas and test the models.

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