Detailed Study of Clustering Technique In Data Mining with Principle of Data Mining

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Abstract

Clustering technique in data mining is a main approach to deal with the data an extraction of useful patterns and knowledge from it. Clustering is involved in the datamining process. Datamining is the way of pulling out the knowledge, information, useful patterns and a reliable data from a huge gigantic amount of raw data as per the needs of the targeted sector. In technical aspects the Data Mining is a way of finding out the useful patterns from the raw data by using the suitable techniques of statistics, Machine learning, and Database techniques. Data mining target two major aspects of extraction of meaning full pattern data for concern of large-scale for better understanding of shapes and profitable patterns of data which impacts globally and the other is small-scale which deals with the lesser impact on the global scale. This paper give a brief overview of Clustering technique under the Data mining process their features and functionality. Majorly concentrate on Clustering technique and their algorithms with the pro’s & con’s and understand the need of clustering and its importance in Data mining process. The Data mining principle is also explained briefly just to build a base to understand the techniques and their importance which has to be discussed
基于数据挖掘原理的数据挖掘中聚类技术的详细研究
聚类技术是数据挖掘中处理数据并从中提取有用模式和知识的一种主要方法。数据挖掘过程涉及聚类。数据挖掘是从海量的原始数据中提取知识、信息、有用的模式和可靠的数据,以满足目标部门的需求。在技术方面,数据挖掘是一种通过使用适当的统计学技术、机器学习技术和数据库技术,从原始数据中发现有用模式的方法。数据挖掘的两个主要方面是提取有意义的全模式数据,以更好地理解影响全球的数据的形状和有利可图的模式,另一个是小规模的,在全球范围内处理较小的影响。本文简要概述了聚类技术在数据挖掘过程中的特点和功能。主要学习聚类技术及其算法的优缺点,了解聚类的必要性及其在数据挖掘过程中的重要性。本文还简要解释了数据挖掘原理,以便为理解将要讨论的技术及其重要性奠定基础
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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