Table of ContentsData Mining TechniquesData mining has three stages:Data Mining Algorithms and TechniquesA. RankingB. ClusteringC. RegressionD. Rule of associationE. Neural NetworksConclusionReferencesData Mining TechniquesWith the development of information technology, a large amount of databases and a huge amount of data in various fields have been generated. Searching across 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 knowledge discovery process, knowledge mining from data, knowledge extraction or data analysis. data or model analysis. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayData 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 these patterns are found, they can then be used to make certain machine learning and predictive analytics decisions. Data mining involves three steps:A. Exploration: First, the data is cleaned and transformed into important variables, then the nature of the data according to the problem is determined.B. Pattern Identification: After exploring, refining and defining the data for the specific variables, the second step is to train pattern identification. Identify and choose the models that make the best prediction.C. Deployment: Finally, the models are used to achieve the desired result.[2]Data Mining Algorithms and TechniquesKnowledge is discovered from the available databases through the use of different types of algorithms and techniques such such as 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 analyses. One of its many methods is the decision tree. The objective is to develop a set of classification rules that will make it possible to answer a question, make a decision or predict a behavior. To begin, a training dataset is developed which contains a certain set of attributes along with the likely outcome. The job of the classification algorithm is to discover how the set of attributes arrives at its conclusion. Different types of classification models are decision tree classification, neural networks, support vector machine.B. Clustering Clustering can be thought of as identifying classes of similar objects. Using clustering techniques, we can further identify dense and sparse regions in the object space and discover the overall distribution pattern and correlations between data attributes. The clustering approach can also be used as an effective way to distinguish groups or classes of objects. But this becomes expensive and hence clustering can be used as a preprocessing approach for subset selection and classification. 26-28, 2017, 978-1-5386-3004-4/17