Sequences, Nets, and Filters of Fuzzy Soft Multi Sets in Fuzzy Soft Multi Topological Spaces

Handbook of Research on Generalized and Hybrid Set Structures and Applications for Soft Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9798-0.ch009 ◽

2016 ◽

pp. 172-190

Author(s):

Anjan Mukherjee ◽

Ajoy Kanti Das

Keyword(s):

Machine Learning ◽

Data Mining ◽

Cluster Analysis ◽

Topological Spaces ◽

Main Task ◽

Filter Convergence ◽

Common Technique ◽

Exploratory Data ◽

Exploratory Data Mining ◽

Convergence Sequence

In this chapter, the authors introduce a new sequence of fuzzy soft multi sets in fuzzy soft multi topological spaces and their basic properties are studied. The concepts of subsequence, convergence sequence and cluster fuzzy soft multi sets of fuzzy soft multi sets are proposed. Actually Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). It is a main task of exploratory data mining and a common technique for statistical data analysis used in many fields including machine learning, pattern recognition, image analysis, information retrieval and bioinformatics. Here the authors define the notions of net and filter and establish the correspondence between net convergence and filter convergence in fuzzy soft multi topological spaces.

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Clustering Techniques

Emerging Trends and Applications in Cognitive Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5793-7.ch009 ◽

2019 ◽

pp. 198-223 ◽

Cited By ~ 1

Author(s):

Harendra Kumar

Keyword(s):

Machine Learning ◽

Data Mining ◽

Image Analysis ◽

Clustering Algorithms ◽

Similarity Measures ◽

Main Task ◽

Clustering Techniques ◽

Data Points ◽

Exploratory Data ◽

Exploratory Data Mining

Clustering is a process of grouping a set of data points in such a way that data points in the same group (called cluster) are more similar to each other than to data points lying in other groups (clusters). Clustering is a main task of exploratory data mining, and it has been widely used in many areas such as pattern recognition, image analysis, machine learning, bioinformatics, information retrieval, and so on. Clusters are always identified by similarity measures. These similarity measures include intensity, distance, and connectivity. Based on the applications of the data, different similarity measures may be chosen. The purpose of this chapter is to produce an overview of much (certainly not all) of clustering algorithms. The chapter covers valuable surveys, the types of clusters, and methods used for constructing the clusters.

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Exploratory Data Mining and Data Cleaning

Journal of the American Statistical Association ◽

10.1198/jasa.2006.s81 ◽

2006 ◽

Vol 101 (473) ◽

pp. 399-399 ◽

Cited By ~ 3

Author(s):

Alan F Karr

Keyword(s):

Data Mining ◽

Data Cleaning ◽

Exploratory Data ◽

Exploratory Data Mining

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Impediments to Exploratory Data Mining Success

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch158 ◽

2008 ◽

pp. 2566-2582

Author(s):

Jeff Zeanah

Keyword(s):

Data Mining ◽

Data Quality ◽

Exploratory Data ◽

Exploratory Data Mining

This chapter discusses impediments to exploratory data mining success. These impediments were identified based on anecdotal observations from multiple projects either reviewed or undertaken by the author and are classified into four main areas: data quality; lack of secondary or supporting data; insufficient analysis manpower; lack of openness to new results. Each is explained, and recommendations are made to prevent the impediment from interfering with the organization’s data mining efforts. The intent of the chapter is to provide an organization with a structure to anticipate these problems and to prevent the occurrence of these problems.

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