Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/490154
Title: Design and Development of Novel Techniques for Clustering and Classification of Data
Researcher: Walse, Rajesh Sudhakar
Guide(s): Kurundkar, G. D.
Keywords: Computer Science
Computer Science Theory and Methods
Engineering and Technology
University: Swami Ramanand Teerth Marathwada University
Completed Date: 2021
Abstract: Data mining is a repetitive process through which advancement is established newlineby automated or manual means through discovery. Data mining is most beneficial in newlinethe scenario of exploratory research, where no fixed conceptions of an important newlineresult are accessible. Data mining is the large volumes of data which seeks fresh, newlineuseful and nontrivial knowledge. It is an initiative in cooperation with humans and newlinemachines. Good outcomes are generated by equalizing the skills of human beings newlinewith machine search capacities while explaining problems and priorities. newlineData classes are categories dependent on data contained inside data newlinerepresenting artifacts and their relationships only. Cluster analyses the purpose of the newlinestudy of the cluster is to connect the objects within the same category and to the newlineobjects in other classes. The greater the resemblance or homogeneity of a group and newlinethe greater the variations between classes, the stronger or simpler is the clustering. A newlinecluster is a group of bodies that are identical and are not alike bodies from different newlineClusters. newlineThe literature includes several clustering algorithms. The selection of the newlineclustering algorithm depends on the form and implementation of accessible data. newlineOnce cluster analyzes are used as a method for explanation or discovery, multiple newlinealgorithms may be attempted for the same data in order to see what data might show. newlineBig cluster techniques may be split down into the following groups in general, such as newlineincomplete techniques, hierarchical techniques, technique based on scale, techniques newlinebased on grid and techniques based on templates. newlineGenetic algorithms are concurrent, powerful and reliable methods for newlinesearching and optimizing, influenced by natural genetics and evolution. When there is newlineincreasing, complex and multimodal search space the genetic algorithm is very newlineefficient. Such algorithms will represent a question with a chromosome, which is like newlinea script. A genetic algorithm is applied by a group of chromosomes or individuals. newlineClustering is already an establis
Pagination: 176p
URI: http://hdl.handle.net/10603/490154
Appears in Departments:Department of Computer Science

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