Basic Concepts in Data Mining

2015 ◽  
pp. 86-98
Keyword(s):  
Data Mining ◽  
Basic Concepts

Application of Rough Set Theory to Data Mining of Condenser Diagnosis in Power Plants

2003 ◽  
Author(s):  
Zhongguang Fu ◽  
Tao Jin ◽  
Kun Yang
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Power Plants ◽  
Rough Set Theory ◽  
A Priori ◽  
Reduction Algorithm ◽  
Useful Knowledge ◽  
Fault Features ◽  
Basic Concepts

Rough set theory is a powerful tool in deal with vagueness and uncertainty. It is particularly suitable to discover hidden and potentially useful knowledge in data and can be used to reduce features and extract rules. This paper introduces the basic concepts and fundamental elements of the rough set theory. A reduction algorithm that integrates a priori with significance is proposed to illustrate how the rough set theory could be used to extract fault features of the condenser in a power plant. Two testing examples are then presented to demonstrate the effectiveness of the theory in fault diagnosis.

scholarly journals Topological properties of a pair of relation-based approximation operators

Filomat ◽  
2017 ◽  
Vol 31 (19) ◽  
pp. 6175-6183
Author(s):  
Yan-Lan Zhang ◽  
Chang-Qing Li
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Equivalence Relations ◽  
Topological Properties ◽  
Upper Approximation ◽  
Approximation Operators ◽  
Rough Approximation ◽  
Basic Concepts

Rough set theory is an important tool for data mining. Lower and upper approximation operators are two important basic concepts in the rough set theory. The classical Pawlak rough approximation operators are based on equivalence relations and have been extended to relation-based generalized rough approximation operators. This paper presents topological properties of a pair of relation-based generalized rough approximation operators. A topology is induced by the pair of generalized rough approximation operators from an inverse serial relation. Then, connectedness, countability, separation property and Lindel?f property of the topological space are discussed. The results are not only beneficial to obtain more properties of the pair of approximation operators, but also have theoretical and actual significance to general topology.

Data Mining in Aviation Safety Data Analysis

2011 ◽  
pp. 178-198 ◽  
Author(s):  
Reima Suomi ◽  
Olli Sjöblom
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Safety Management ◽  
Safety Data ◽  
Aviation Safety ◽  
Important Application ◽  
Civil Aviation ◽  
Basic Concepts ◽  
In The Beginning ◽  
Mining Tools

This chapter introduces aviation safety data analysis as an important application area for data mining. In the beginning of the chapter, the reader is introduced to the basic concepts of data mining. After that, the field of aviation safety management is discussed, and in that connection data mining is identified as a key technology to study through flight incidents reports. Afterwards the test runs for four data mining products, for possible use in the Finnish civil aviation authority, are described in detail. However, before the testing of tools the preparation of the test data for the tools is described in detail. The chapter ends with conclusions that tell that even sophisticated data mining tools are just tools: they do not provide any automatic tools, but skilled users can use them for searching clues in the data.

Research on Data Mining and Apriori Algorithm

2012 ◽  
Vol 546-547 ◽  
pp. 497-502 ◽  
Author(s):  
Yu Lian Gai
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Computer Technology ◽  
Point Of View ◽  
Main Task ◽  
Apriori Algorithm ◽  
Mining Technology ◽  
Basic Concepts

From information technology point of view, analyzed the theoretical and practical background of data mining processing massive amounts of data. Based on the definition and basic concepts of data mining, discussed and analyzed 5 aspects of its main task. Described the data mining process comprehensively. Integrating with the problems within the Apriori Algorithm, described the improvements to the Apriori algorithm. And, in the end, summarized the role of data mining technology in promoting the development of computer technology.

Mining Association Rules

2010 ◽  
pp. 647-673 ◽  
Author(s):  
Mihai Gabroveanu
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Knowledge Discovery In Databases ◽  
Discovery Process ◽  
Fuzzy Association Rules ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Basic Concepts

During the last years the amount of data stored in databases has grown very fast. Data mining, also known as knowledge discovery in databases, represents the discovery process of potentially useful hidden knowledge or relations among data from large databases. An important task in the data mining process is the discovery of the association rules. An association rule describes an interesting relationship between different attributes. There are different kinds of association rules: Boolean (crisp) association rules, quantitative association rules, fuzzy association rules, etc. In this chapter, we present the basic concepts of Boolean and the fuzzy association rules, and describe the methods used to discover the association rules by presenting the most important algorithms.

Research of Data Base and Data Mining in CRM

2014 ◽  
Vol 543-547 ◽  
pp. 2988-2991
Author(s):  
Xiang Min Wu ◽  
Cheng Lin Zhao ◽  
Pan Cao
Keyword(s):  
Data Mining ◽  
Computer Science ◽  
Data Base ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Research Field ◽  
Customer Relationship ◽  
Basic Concepts ◽  
Active Research

Customer Relationship Management (CRM) is becoming the focus of enterprise and an active research field of computer science. The ariticle introduces some basic concepts about CRM and data mining, and some benefits brought by data mining in CRM. At the end it points out how to apply data mining applications in CRM.

Instance Selection

2011 ◽  
pp. 621-624
Author(s):  
Huan Liu ◽  
Lei Yu
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Data Reduction ◽  
State Of The Art ◽  
Effective Means ◽  
The State ◽  
Digital Data ◽  
Instance Selection ◽  
Basic Concepts ◽  
Digital Data Storage

The amounts of data have become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces the basic concepts of instance selection and its context, necessity, and functionality. The article briefly reviews the state-of-the-art methods for instance selection.

Data Mining and Warehousing in Pharma Industry

2011 ◽  
pp. 239-244
Author(s):  
Andrew Kusiak ◽  
Shital C. Shah
Keyword(s):  
Data Mining ◽  
Pharmaceutical Industry ◽  
Paradigm Shift ◽  
Data Warehousing ◽  
Data Modeling ◽  
Data Driven ◽  
Basic Concepts ◽  
Main Factor

Most processes in pharmaceutical industry are data driven. Company’s ability to capture the data and making use of it will grow in significance and may become the main factor differentiating the industry. Basic concepts of data mining, data warehousing, and data modeling are introduced. These new data-driven concepts lead to a paradigm shift in pharmaceutical industry.

The Method Research on Data Mining of Concept Lattice

2015 ◽  
Vol 713-715 ◽  
pp. 1970-1973
Author(s):  
Chun Liu ◽  
Dong Xing Wang ◽  
Kun Tan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Basic Theory ◽  
Formal Concept ◽  
Concept Lattices ◽  
Hasse Diagrams ◽  
Basic Concepts

Concept lattice in essence describe the links between objects and attributes,demonstratesthe generalization and specialization of concepts. The corresponding Hasse diagrams realize the visualization of the data. At present, formal concept analysis has been extensively studied and applied to many areas, such asinformation retrieval, machine learning andsoftware engineering. Based on the above reasons, it is necessary to research the methods of latticeconcept of data mining. This paper is divided into three parts; the first part introduces the basic concepts of data mining. The second part introduces the basic theory of concept lattices. The last part focuses on the application of concept in data mining.

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