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.

Comparing Partitions by Means of Fuzzy Data Mining Tools

2012 ◽  
pp. 337-350 ◽  
Author(s):  
Carlos Molina ◽  
Belén Prados ◽  
María-Dolores Ruiz ◽  
Daniel Sánchez ◽  
José-María Serrano
Keyword(s):  
Data Mining ◽  
Fuzzy Data ◽  
Comparing Partitions ◽  
Mining Tools

scholarly journals Analysis of Clustering for Grouping of Productive Industry by K-Medoid Method

2017 ◽  
Vol 2 (1) ◽  
pp. 26
Author(s):  
Indah Cahya Dewi ◽  
Bara Yuda Gautama ◽  
Putu Arya Mertasana
Keyword(s):  
Data Mining ◽  
Product Quality ◽  
The Third ◽  
Data Grouping ◽  
Clustering Patterns ◽  
Good Cluster ◽  
Cluster Quality ◽  
Existing Data

With the number of existing data, would have difficulty in doing the classification and the classification of the existing data. To resolve the issue, one way to do clustering is with data mining using clustering technique. The purpose of this research is the importance of knowing the pattern of the production of an industry that can provide the decision and the construction of clustering patterns for development and industrial progress. The results of this research can provide recommendations to improve the development of industry, help the owners of industry to develop the industry to an increase in the number of production and product quality, improve the competitiveness of the owner of the industry in developing its products. In this research will use the K-Medoids algorithm for data grouping of the industry so that it will be found the information that can be used for the recommendations of the improvement of marketing. The results of clustering with the number of cluster 3 produces the first group contains 85 members, the second group contains 222 members and the third group numbered 3 members. The third group are classified as productive because it has a combination of the value of the production of the most high the results of clustering have the quality of purity worth 1 means good cluster quality.

VISUALIZATION-BASED SELECTION-AIMED DATA MINING WITH FUZZY DATA

2006 ◽  
Vol 05 (04) ◽  
pp. 611-621 ◽  
Author(s):  
ALEXANDER V. LOTOV
Keyword(s):  
Data Mining ◽  
Financial Planning ◽  
Web Application ◽  
Relational Databases ◽  
Environmental Planning ◽  
Uncertain Data ◽  
Fuzzy Data ◽  
New Development ◽  
The Web ◽  
Machinery Design

The paper is devoted to a visualization-based method for exploration of relational databases that contain large volumes of uncertain data. The visualization is aimed at exploration of properties of the data and selecting a small number of interesting items from the database. The method introduced here is a new development of the Reasonable Goals method, which has already been implemented on Internet in the form of the Web application server. Thus, the new method can be applied on Internet, too. It can be used for selection-aimed data mining in various fields including environmental planning, machinery design, financial planning (including credit operations), biology and medicine.

Looking for Information in Fuzzy Relational Databases Accessible Via Web

2009 ◽  
pp. 2448-2471
Author(s):  
Carmen Martínez-Cruz ◽  
Ignacio José Blanco ◽  
Maria Amparo Vila
Keyword(s):  
Semantic Web ◽  
Relational Databases ◽  
Management Systems ◽  
Database Schema ◽  
Fuzzy Data ◽  
Web Interfaces ◽  
Wide Range ◽  
Fuzzy Database

The Semantic Web has resulted in a wide range of information (e.g., HML, XML, DOC, PDF documents, ontologies, interfaces, forms, etc.) being made available in semantic queries, and the only requirement is that these are described semantically. Generic Web interfaces for querying databases (such as ISQLPlus ©) are also part of the Semantic Web, but they cannot be semantically described, and they provide access to one or many databases. In this chapter, we will highlight the importance of using ontologies to represent database schemas so that they are easier to access. The representation of the fuzzy data in fuzzy databases management systems (FDBMS) has certain special requirements, and these characteristics must be explicitly defined to enable this kind of information to be accessed. In addition, we will present an ontology which allows the fuzzy structure of a fuzzy database schema to be represented so that fuzzy data from FDBMS can also be available in the Semantic Web.

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.

Looking for Information in Fuzzy Relational Databases Accessible Via Web

2011 ◽  
pp. 301-323
Author(s):  
Carmen Martínez-Cruz ◽  
Ignacio José Blanco ◽  
M. Amparo Vila
Keyword(s):  
Semantic Web ◽  
Relational Databases ◽  
Management Systems ◽  
Database Schema ◽  
Fuzzy Data ◽  
Web Interfaces ◽  
Wide Range ◽  
Fuzzy Database

The Semantic Web has resulted in a wide range of information (e.g., HML, XML, DOC, PDF documents, ontologies, interfaces, forms, etc.) being made available in semantic queries, and the only requirement is that these are described semantically. Generic Web interfaces for querying databases (such as ISQLPlus©) are also part of the Semantic Web, but they cannot be semantically described, and they provide access to one or many databases. In this chapter, we will highlight the importance of using ontologies to represent database schemas so that they are easier to access. The representation of the fuzzy data in fuzzy databases management systems (FDBMS) has certain special requirements, and these characteristics must be explicitly defined to enable this kind of information to be accessed. In addition, we will present an ontology which allows the fuzzy structure of a fuzzy database schema to be represented so that fuzzy data from FDBMS can also be available in the Semantic Web.

scholarly journals Towards a New Extracting and Querying Approach of Fuzzy Summaries

2018 ◽  
pp. 317-339
Author(s):  
Ines Benali-Sougui ◽  
Minyar Sassi Hidri ◽  
Amel Grissa-Touzi
Keyword(s):  
Relational Databases ◽  
Formal Model ◽  
Database Management System ◽  
Fuzzy Data ◽  
Formal Concepts ◽  
A New Technique ◽  
The Impact ◽  
Relational Database Management ◽  
Flexible Querying

Diversification of DB applications highlighted the limitations of relational database management system (RDBMS) particularly on the modeling plan. In fact, in the real world, we are increasingly faced with the situation where applications need to handle imprecise data and to offer a flexible querying to their users. Several theoretical solutions have been proposed. However, the impact of this work in practice remained negligible with the exception of a few research prototypes based on the formal model GEFRED. In this chapter, the authors propose a new approach for exploitation of fuzzy relational databases (FRDB) described by the model GEFRED. This approach consists of 1) a new technique for extracting summary fuzzy data, Fuzzy SAINTETIQ, based on the classification of fuzzy data and formal concepts analysis; 2) an approach of assessing flexible queries in the context of FDB based on the set of fuzzy summaries generated by our fuzzy SAINTETIQ system; 3) an approach of repairing and substituting unanswered query.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

XML Modeling of Fuzzy Data with Relational Databases

2011 ◽  
Vol 34 (2) ◽  
pp. 291-303 ◽  
Author(s):  
Li YAN ◽  
Zong-Min MA ◽  
Jian LIU ◽  
Fu ZHANG
Keyword(s):  
Relational Databases ◽  
Fuzzy Data
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