Combining Rough Clustering Schemes as a Rough Ensemble
One of the challenges of big data is to combine results of data mining obtained from a distributed dataset. The objective is to minimize the amount data transfer with minimum information loss. A generic combination process will not necessarily provide an optimal ensemble of results. In this paper, we describe a rough clustering problem that leads to a natural ordering of clusters. These ordered rough clusterings are then combined while preserving the properties of rough clustering. A time series dataset of commodity prices is clustered using two different representations to demonstrate the ordered rough clustering process. The information from the ordering of clusters is shown to help us retain salient aspects of individual rough clustering schemes.
KeywordsClustering Ensemble Rough sets Granular computing Financial time series Volatility
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