IndexData mining techniquesData mining involves three steps:Data mining algorithms and techniquesA. ClassificationB. ClusteringC. Regression D. Association ruleE. Neural NetworksConclusionReferencesData Mining TechniquesWith the development of information technology, a large amount of databases and a huge amount of data have been generated in various areas. Searching in different databases and information technologies has always given rise to an approach to store and manipulate this valuable data for further decision making. Data mining is a process of extracting useful information and patterns from a large amount of data and is called the process of knowledge discovery, extracting knowledge from data, knowledge extraction, or data analytics or pattern analysis. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Data mining is a logical process that searches for useful data from a large amount of raw data. The main goal of this technique is to find previously unknown patterns. Once found, these patterns can be further used to make certain decisions for machine learning and predictive analytics. Data mining involves three steps:A. Exploration: First the data is cleaned and transformed into important variables and then the nature of the data is determined based on the problem.B. Model identification: After exploring, refining, and defining the data for specific variables, the second step is to form model identification. Identify and choose the models that provide the best prediction.C. Deployment: Finally the models are used for the desired outcome.[2]Data Mining Algorithms and TechniquesKnowledge is discovered from available databases with the use of different types of algorithms and techniques like classification, clustering, regression, artificial intelligence, neural networks , Association Rules, Decision Trees, Genetic Algorithm, Nearest Neighbor Method etc.A. Classification Classification is a data mining technique that assigns categories to a collection of data to facilitate more accurate predictions and analysis. One of its many methods is the decision tree. The goal is to set classification rules that answer a question, make decisions, or predict behavior. To begin with, a set of training data is developed that contains a given set of attributes and the likely outcome. The task of the classification algorithm is to find out how the set of attributes reaches its conclusion. Different types of classification models are decision tree classification, neural networks, Support Vector Machine.B. Clustering Clustering can be defined as the identification of similar classes of objects. Using clustering techniques we can further identify dense and sparse regions in object space and discover overall distribution patterns and correlations between data attributes. The clustering approach can also be used as an effective means of distinguishing groups or classes of objects. However, it becomes expensive, so clustering can be used as a preprocessing approach for attribute subset selection and classification. For example, to form a group of customers based on purchasing patterns, classify genes with similar functionality. Partitioning methods, agglomerative (divisive) hierarchical methods Density-based methods, grid-based methods Model-based methods are the different types of clustering methodsC. RegressionThe regression technique can be adapted for prediction. Regression analysis can be used to. 26-28, 2017, 978-1-5386-3004-4/17