Details

Autor(en) / Beteiligte

• Lohani, Q. M. Danish
• Solanki, Rinki
• Muhuri, Pranab K.

Titel

Novel Adaptive Clustering Algorithms Based on a Probabilistic Similarity Measure Over Atanassov Intuitionistic Fuzzy Set

Ist Teil von

• IEEE transactions on fuzzy systems, 2018-12, Vol.26 (6), p.3715-3729

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2018

Quelle

IEEE Xplore

Beschreibungen/Notizen

• This paper presents a novel probabilistic similarity measure (PSM) for Atanassov intuitionistic fuzzy sets. It then exploits PSM to propose an adaptive probabilistic similarity degree and develops the novel probabilistic λ-cutting algorithm for clustering. Further, the probabilistic distance measure (obtained from the PSM) is used to develop a new clustering technique, which we have named "probabilistic intuitionistic fuzzy c-mean (PIFCM) algorithm". Simulation experiments have been conducted over a variety of datasets including UCI machine learning datasets and realworld car dataset. The results obtained have been thoroughly compared with other well-known clustering techniques such as fuzzy c-mean (FCM), intuitionistic fuzzy c-mean, association coefficient method, and λ-cutting method. Based upon the experimental results, it can be concluded that our probabilistic λ-cutting algorithm and PIFCM algorithm outperform their existing counterparts.