With enormous development of digital technology, data is being generated at rapid rate with various application domains. Data has to be extracted or filtered to find useful information. A basic concept for these tasks and applications are the distance measures to effectively determine how similar two objects are. In this paper, a novel similarity measure for clustering text documents is proposed using the cardinality of the terms in the documents. The bench mark algorithm k-medoids is used for clustering task. The results obtained from the proposed distance measure are compared with other standard distance measures like Manhattan, Euclidean distance measure. Dunn Index is used to analyze the cluster validation of the results obtained from the distance measure.
The properties and activities of chemical compounds can be understood by computing topological descriptors of molecular compounds. We investigate the physical and topological aspects of crystal structure of metal-insulator transition superlattice (GST-SL) in this study. Recently, researchers have turned their attention to modifying this substance into a form that is useful for human life. Metalinsulator transition superlattices (GST-SL) are also useful as twodimensional (2D) transition metal dichalcogenides (TMDs) in the form of thin films. For this Superlattice Network SLη, we calculate open and closed neighbourhood degree sum based on topological indices.
Data is being generated at an increasing rate in a variety of fields as science and technology advance. The generated data are being saved for future decision-making. Data mining is the process of extracting patterns and useful information from massive amounts of data. The distance measure, which is used to calculate how different two objects are from one another, is one such instrument. We have conducted a comprehensive survey of how the distance measures behave when employed with different algorithms. Furthermore, the effectiveness and performance of some novel similarity measures proposed by other authors are investigated.
Text Clustering is a text mining technique which divides the given set of text documents into significant clusters. It is used for organizing a huge number of text documents into a well-organized form. In the majority of the clustering algorithms, the number of clusters must be specified apriori, which is a drawback of these algorithms. The aim of this paper is to show experimentally how to determine the number of clusters based on cluster quality. Since partitional clustering algorithms are well-suited for clustering large document datasets, we have confined our analysis to a partitional clustering algorithm.
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