A Review on Clustering of Uncertain Data
Manisha Padole, Sonali Bodkhe
Clustering is a crucial task in data mining. There are so many techniques for data mining which are continuously emerging since the concept of data mining has been taken into account. All the clustering methodologies focus on certain data or the known data. But very few methodologies focus on the clustering of uncertain data. By considering the uncertain data for clustering, the results of clustering algorithm get affected and can show any unwanted results. For example, in the sensor based applications the output can be uncertain if there is any errors in input (e.g. noise occurred during capturing information). In such situations, the algorithm must be strong enough to consider the uncertain data because the uncertain data is also important. As there is continuous increase in data accumulation in the databases we need to be more aware about to handle uncertain data. In this paper we will discuss about the data uncertainty and the various approaches to handle and cluster the uncertain data.
Keywords: Clustering, Uncertain data, FCM, KL-Divergence, clustering algorithm
Edition: Volume 5 Issue 5, May 2016
Pages: 1980 - 1983