This function is also optimized to find the cluster centers and the membership matrix. This function is incorporated with new weights for each labeled data so that the system can check whether a labeled data point is corrected or not. At first, a new objective function is proposed. The key contributions of the paper are briefly summarized as follows. The novelty of TS3FCM against the other safe semi-supervised fuzzy clustering algorithms lies at the isolated processes of finding trusted labeled data and performing semi-supervised fuzzy clustering. The key motivation behind TS3FCM is to handle the drawbacks of the related safe semi-supervised fuzzy clustering algorithms regarding huge computational time. In this research, we propose a new algorithm for the Data partition with confidence problem named as Trusted Safe Semi-Supervised Fuzzy Clustering Method (TS3FCM). However, this process often takes much computational time and sometimes produces unreasonable results. Safe semi-supervised fuzzy clustering has been used extensively in recent years to tackle with this problem by adding the concept of a local graph between labeled and unlabeled data so that wrong labeled data has small impact to the final clusters. This would degrade the performance of final clustering results because of noises and outliers being occurred. It is known that there may be some data with less confidence (wrong values, incorrect attribute types, irrelevant domain ranges, etc.) existed in the whole dataset due to the data gathering process. In the case of the selection of books that must be added to the library and to minimize the books that are rarely borrowed so as not to cause a buildup of books that are rarely borrowed, so there is a space for books to be added into the library.ĭata partition with high confidence is one of the main concentration of researchers in Soft Computing for many years. The information of grouping this book data can be used by the library. With the use of this K-means Clustering method, the final result obtained consists of member of cluster 1 as many as 19 members, member of cluster 2 as many as 22 members, and member of cluster 3 as many as 19 members. The output produced consists of 3 clusters, those are (C1) the most frequently borrowed book, (C2) book that is often borrowed, and (C3) book that is rarely borrowed. On this K-means clustering algorithm, the variables used as input are : NIM, Name, Book Title and Author. This research aims to group the book information which is contained in Universitas Prima Indonesia, by using K-means clustering method. Clustering is a process of sorting out a data set to become separate cluster groups and each has similarities, and aims to group the data into one cluster.

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