The Research for Discovering Monthly Fuzzy Patterns

Discovering patterns that are fuzzy in nature from temporal datasets is an interesting data mining problems. One
of such patterns is monthly fuzzy pattern where the patterns exist in a certain fuzzy time interval of every
month. It involves finding frequent sets and then association rules that holds in certain fuzzy time intervals, viz.
beginning of every months or middle of every months, etc. In most of the earlier works, the fuzziness was userspecified.
However, in some applications, users may not have enough prior knowledge about the datasets under
consideration and may miss some fuzziness associated with the problem. It may be the case that the user is
unable to specify the same due to limitation of natural language. In this article, we propose a method of finding
patterns that holds in certain fuzzy time intervals of every month where fuzziness is generated by the method
itself. The efficacy of the method is demonstrated with experimental results.