An significant data mining issue is the discovery of patterns from temporal data sets that are fuzzy in nature. One of these patterns is a monthly fuzzy pattern where there are patterns in a certain monthly fuzzy time period. It includes finding frequent sets and then rules of association that hold in some fuzzy intervals of time, viz. Starting every month or in the middle of the month, etc. The fuzziness was defined by users in most of the earlier works. However, users may not have adequate prior knowledge of the datasets under consideration in some applications and may miss some fuzziness associated with the problem. It could be the case that due to a constraint in natural language, the user is unable to specify the same. In this article , we propose a method of finding patterns that take place each month in some fuzzy time intervals where the method itself generates fuzziness. The feasibility of the procedure is shown by experimental findings.
Author (s) Details
Dr. M. Shenify
College of Computer Science and IT, Albaha University, Albaha, Saudi Arabia.
Dr. F. A. Mazarbhuiya
Department of Mathematics, School of Fundamental and Applied Sciences, Assam Don Bosco University, INDIA
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