Abstract
In order to improve the quality of overlapping detection, Rough K-Means (RKM) was proposed as the first kind of rough clustering algorithm. It was found that this recent RKM algorithm known as π RKM is the most powerful and effective version in which there is an increase in the number of objects that are correctly clustered and a decrease in the number objects that are incorrectly clustered compared to the issues which the previous RKM had. However, there are challenges associated with the clustering process which uses RKM as a result of the difficulty in establishing a standard measure for reducing the effect of local outlier objects on a means function. Therefore, the RKM algorithm is refined in this study to address the problem. Through this study we contribute two components. Firstly, we intend to employ the use of Local Outlier Factor (LOF) technique for the discrimination of a number of objects as outliers and secondly, we propose to reduce the effect of local outliers on means function by using a weight. The result of the experiments which were performed through the use of synthetic and real life datasets prove that there is an improvement in the quality of overlapping detection when compared to recent versions.
Highlights
K-Means which is regarded as Hard K-Means is a clustering algorithm that is simple and unsupervised (Hartigan and Wong, 1979)
K-Means is regarded in the literature as of the frequently used clustering algorithms which for over 50 years has been in use (Jain, 2010; Xiao and Yu, 2012) several domains of application (Peters et al, 2013)
In order to address the shortcomings of this algorithm soft clustering algorithms like Fuzzy CMeans (Bezdek and Harris, 1978) and its derivatives such as Possibilistic C-Means (PCM) (Krishnapuram and Keller, 1993)
Summary
K-Means which is regarded as Hard K-Means is a clustering algorithm that is simple and unsupervised (Hartigan and Wong, 1979). The first algorithm to adopt this approach is the Rough K-Means (RKM) (Lingras and West, 2004). The aim of this algorithm is to distinguish objects that overlap between positive clusters based on the process of Hard K-Means. Some of the improved versions are introduced to achieve satisfactory RKM clustering results such as that in Peters (2006, 2012) which minimize the effect of the objects in the upper regions against the objects in the lower region.
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