scispace - formally typeset
Proceedings ArticleDOI

Covering based rough clustering of sequential data

TLDR
Covering based rough set is an extension of rough set approach in which the equivalence relation has been relaxed and this method is based on coverings rather than partitions, which makes it more flexible than rough sets and it is more convenient for dealing with complex applications.
Abstract
Rough set approach is a very useful tool to handle the unclear and ambiguous data In this approach, a set's boundary region is used to express the incorrectness Based on an equivalence relation, we can have upper and lower approximations for rough set As rough sets make use of the equivalence relation property, they remain rigid Rough set theory becomes a time consuming process because we need to find all the equivalence classes It is unreliable and inefficient for real time applications where the data sets may be very large In this paper, we provide a solution to this problem with covering based rough set approach Covering based rough set is an extension of rough set approach in which the equivalence relation has been relaxed This method is based on coverings rather than partitions This makes it more flexible than rough sets and it is more convenient for dealing with complex applications The purpose of covering based rough set is to get more number of overlapping We uses covering based similarity measure which gives better results as compared to rough set which uses set and sequence similarity measure

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References
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Journal ArticleDOI

Data clustering: a review

TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Journal ArticleDOI

Rough set approach to incomplete information systems

TL;DR: This work proposes reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making, and shows how to find decision rules directly from such an incomplete decision table.
Journal ArticleDOI

On Three Types of Covering-Based Rough Sets

TL;DR: The relationships among the definable sets are investigated, and certain conditions that the union of the neighborhood and the complementary neighborhood is equal to the indiscernible neighborhood are presented.
Book ChapterDOI

A New Rough Sets Model Based on Database Systems

TL;DR: This paper proposes a new rough sets model and redefine the core attributes and reducts based on relational algebra to take advantages of the very efficient set-oriented database operations.
Proceedings ArticleDOI

On three types of covering-based rough sets via definable sets

TL;DR: Three kinds of covering generalized rough sets for dealing with the vagueness and granularity in information systems are studied and the relationships among these three types of covering rough sets are explored.