A Study of Fuzzy and Non-fuzzy clustering algorithms on Wine Data

Abstract

Data clustering has been considered as the most important exploratory data analysis method used to extract the unknown valuable information from the large volume of data for many real time applications in Data Mining technology. Most of the clustering techniques proved their efficiency in many fields such as decision making systems, medical sciences, earth sciences, etc. partition based clustering is one of the main approaches used in clustering. This work reports the results of classification performance of four such widely used algorithms namely K-means (KM) or Hard c-means, Fuzzy C-means, Fuzzy Possibilistic c-Means (FPCM) and Possibilistic Fuzzy c-Means (PFCM) clustering algorithms. Well known data set from UCI machine learning repository are considered to test the algorithms. The efficiency of clustering output is compared with the results observed from the repository. The experimental results demonstrate that FCM, FPCM and PFCM gives the similar percentage of correctness and classification performance.FCM, FPCM and PFCM results are better than K-means. The experimental results prove that fuzzy clustering algorithms are better than non-fuzzy clustering algorithm.

Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques

“…Fuzzy c-means is one of the most popular clustering algorithms studied (Venkataramana et al , 2017). Data clustering consists of evaluating a nearness of data.…”