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Fuzzy C-means Clustering with Temporal-based Membership Function


  • School of Computing, Universiti Utara Malaysia, 06010 Sintok, Kedah,, Malaysia


Objective: In this paper, a method is proposed to create clusters depending on temporal information. Despite its popularity, the FCM algorithm does not utilize temporal information in creating clusters, hence affecting the accuracy of clustering. This paper presents an improved Fuzzy C-means algorithm that incorporates temporal information into the membership function used for clustering. Methods: The proposed FCM algorithm employs temporal neighbouring of data points as the base of clustering. In order to evaluate the algorithm, experimental analysis was performed on three multi-labelled datasets, including a clinical free text (medical), textual email messages (Enron), and Bibtex. Finding: The experimental results show that the proposed function contributes a smaller value of objective function while using a minimum number of iterations. Application: The proposed work will benefit data mining in various domains such as information retrieval, healthcare, business management and many others. This is due to its ability in grouping data- points that are not mutually exclusive.


Fuzzy C-mean, Data Clustering, Data Mining, Multi-labelled Data.

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