Abstract

Data mining is knowledge discovery process. It has to deal with exact information and inexact information. Statistical methods deal with inexact information but it is based on likelihood. Zadeh fuzzy logic deals with inexact information but it is based on belief and it is simple to use. Fuzzy logic is used to deal with inexact information. Data mining consist methods and classifications. These methods and classifications are discussed for both exact and inexact information. Retrieval of information is important in data mining. The time and space complexity is high in big data. These are to be reduced. The time complexity is reduced through the consecutive retrieval (C-R) property and space complexity is reduced with blackboard systems. Data mining for web data based is discussed. In web data mining, the original data have to be disclosed. Fuzzy web data mining is discussed for security of data. Fuzzy web programming is discussed. Data mining, fuzzy data mining, and web data mining are discussed through MapReduce algorithms.

Highlights

  • Data mining is an emerging area for knowledge discovery to extract hidden and useful information from large amounts of data

  • The consecutive retrieval (C-R) cluster property is a presorting to store the datasets for clusters

  • The time and space complexity shall be reduced through the consecutive retrieval (C-R) cluster property

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Summary

Introduction

Data mining is an emerging area for knowledge discovery to extract hidden and useful information from large amounts of data. Information has to be retrieved within a reasonable time period for big data analysis. This may be achieved through the consecutively retrieval (C-R) of datasets for queries. C-R property is extended to cluster analysis. The time and space complexity shall be reduced through the consecutive retrieval (C-R) cluster property. The web programming has to handle incomplete information. Web intelligence is an emerging area and performs data mining to handle incomplete information. Fuzzy web programming is discussed to deal with data mining using fuzzy logic. The fuzzy algorithmic language, called FUZZYALGOL, is discussed to design queries in data mining. Some examples are discussed for web programming with fuzzy data mining

Data mining
Association rule
Data mining using C-R cluster property
Design of parallel C-R cluster property
Visual design for parallel cluster
Parallel cluster design through genetic approach
Parallel cluster design cluster analysis
Design of retrieval of cluster using blackboard system
Fuzzy data mining
Negation
Fuzzy security for data mining
Web intelligence and fuzzy data mining
Conclusion
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