A Novel Uncertain Fuzzy C-Means Clustering Technique Using Genetic Algorithm (UFCM-GA)
Sandhya Rawat, Ajit Kumar Shrivastava, Amit Kumar Saxena · 2013
In computer science, uncertain data is the notion of data that contains specific uncertainty. Uncertain data is typically found in the area of sensor networks. When representing such data in a database, some indication of the probability of the various values. There is a growing awareness of the need for database systems to be able to handle and correctly process data with uncertainty. The uncertainty is normally evaluated as probability density functions. Beyond storing and processing such data in a DBMS, it is necessary to perform other data analysis tasks such as data mining. Fuzzy clustering is a class of algorithms for cluster analysis in which the allocation of data points to clusters is not hard (all-or-nothing) but in the same sense as fuzzy logic. A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution. In this paper we proposed Uncertain Fuzzy C-Means Clustering using Genetic Algorithm (UFCM-GA). Our proposed mechanism is applicable to any uncertainty region. The experimental results analysis showed the effectiveness compared with existing works.