Abstract
Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.
Highlights
Clustering is a machine learning technique that involves the grouping of data points into different clusters, where data points in the same cluster have a higher degree of similarity and any two data points in two different clusters have a lower degree of similarity
In order to evaluate the performance of the proposed MC-fuzzy C-means (FCM) algorithm, we tested it using several UCI-benchmark datasets [13], described in Table 1 and compared it with FCM and FCMT2I
This study proposed a novel clustering algorithm FCM using multiple fuzzification coefficients, corresponding to each element of the dataset
Summary
Clustering is a machine learning technique that involves the grouping of data points into different clusters, where data points in the same cluster have a higher degree of similarity and any two data points in two different clusters have a lower degree of similarity. The fuzzy set theory provides an appropriate method of representing and processing these types of data elements, along with the concept of membership function defined in the range [0,1]. In this concept, each element can belong to more than one cluster. Many new methods based on the FCM algorithm were introduced, in order to overcome the limitations and improve the clustering ability of this algorithm in different cases. Algorithms 2020, 13, 158 elements and cluster center, use different fuzzy measures for the membership of an element to a cluster or modify the exponential parameter for fuzzifying [3,5]. The rest of this study is organized as follows: Section 2 presents the FCM Algorithm and improvement ideas; Section 3 describes the proposed novel MC-FCM algorithm; and Section 4 provides the experimental results for algorithm evaluation
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