Data Mining Techniques

IJCSET |September 2012 | Vol 2, Issue 9, 1402-1405

www.ijcset.net | ISSN:2231-0711

A Process to Comprehend Different Patterns of Data Mining Techniques for Selected Domains

Reshma Sultana, Vani, Managina Deepti, PV Bhaskhar, Pedada Satish, Koppala KVP Sekhar

Abstract: One of the important problems in data mining is the Classification rule learning which involves finding rules that partition given data into predefined classes. In the data mining domain where millions of records and a large number of attributes are involved, the execution time of existing algorithms can become prohibitive, particularly in interactive applications. Data mining is a field at the intersection of computer science and statistics, is the process that attempts to discover patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Keywords: FP-Growth, Apriori Algorithm, SS-BE, SS-MB

INTRODUCTION:

An enormous amount of data stored in databases and data warehouses, it is increasingly important to develop [1] powerful tools for analysis of such data and mining interesting knowledge from it. Data mining [4] is a process of inferring knowledge from such huge data. It has five major components: - Association rules - Classification or clustering - Characterization & Comparison - Sequential Pattern Analysis. - Trend Analysis An association rule [5] is a rule which implies certain association relationships among a set of objects in a database. In this process we discover a set of...