Abstract— The term ‘Clustering’ means grouping of input datasets into subsets, which is commonly called ’clusters’ .The elements in the clusters, are somewhat similar. Many clustering algorithms require the specification of the number of clusters to be produced, prior to execution of the algorithm. The specified method proposes a simple and efficient clustering method. This paper presents an ov...

Partial discharge (PD) measurement and recognition is a significant tool for potential failure diagnosis of the high-voltage equipment. This paper proposes the application of grey clustering approach (GCA) to recognize partial discharge patterns of the high-voltage equipment. The PD patterns are measured by using a commercial PD detector. A set of features, used as operators, for each PD patter...

In pattern recognition, clustering is a powerful technique that can be used to find the identical group of objects from a given dataset. It has proven its importance in various domains such as bioinformatics, machine learning, pattern recognition, document clustering, and so on. But, in clustering, it is difficult to determine the optimal cluster centers in a given set of data. So, in this pape...

Data clustering is typically considered a subjective process, which makes it problematic. For instance, how does one make statistical inferences based on clustering? The matter is di0erent with pattern classi1cation, for which two fundamental characteristics can be stated: (1) the error of a classi1er can be estimated using “test data,” and (2) a classi1er can be learned using “training data.” ...

Clustering combination uses more than one clustering method with identical pattern features to improve the clustering performance. In general clustering is an optimization procedure based on a specific clustering criterion, so clustering combination can be regarded as a technique that constructs and processes multiple clustering criteria rather than a single criterion. We propose two methods of...

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...

The smart grid is an important application field of the Internet of things. This paper presents a method of user electricity consumption pattern analysis for smart grid applications based on the audio feature EEUPC. A novel similarity function based on EEUPC is adapted to support clustering analysis of residential load patterns. The EEUPC similarity exploits features of peaks and valleys on cur...

In object-oriented or object-relational databases such as multimedia databases or most XML databases, access patterns are not static, i.e., applications do not always access the same objects in the same order repeatedly. However, this has been the way these databases and associated optimisation techniques such as clustering have been evaluated up to now. This paper opens up research regarding t...

Students can have various characteristics and learning patterns. By understanding the pattern of individual students, teachers provide individualized strategies based on students' needs. Students' patterns may experience changes depending their conditions during process. If analysis is only run once, then progress in student throughout process cannot be recognized. On other hand, periodical exp...