An Interval-Based Framework for Fuzzy Clustering Applications


The most goal of using knowledge with interval nature is to represent numeric information endowed with impreciseness, which are normally captured from measures of world. But, so as to try and do this, it's necessary to adapt real-valued techniques to be applied on interval-primarily based knowledge. For interval-primarily based clustering applications, for example, it is necessary to propose an interval-based mostly distance and conjointly to adapt clustering algorithms to be employed in this context. So, in this paper, we tend to aim to supply a platform for performing clustering applications using interval-based mostly information, together with distance measure, clustering algorithms, and validation indexes. During this case, we have a tendency to adapt an interval-based mostly distance referred to as $d_km$, and we propose two interval-based mostly fuzzy clustering algorithms: Interval-primarily based FcM and interval-based mostly ckMeans, and three interval-based mostly validation indexes. In order to validate the proposed interval-based framework, an empirical analysis was conducted using seven clustering datasets, three real and 4 synthetic interval datasets. The empirical analysis is based on an external cluster validity index, corrected rand, and six internal-based validation indexes, in that 3 of them can be used in their original proposal and three are proposed during this paper. The obtained results show the usefulness of the proposed interval-primarily based framework for interval-based mostly clustering problems.

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