Data mining is a technique which is used to extract the exact information from the bulk of data collected from different data sources. Data mining is a study of algorithms which are used to find the patterns in large data sets. As the organisation is using different data collecting tools, therefore data mining has become an integral part of the industry that helps in engaging large dataset and extract the required information from it by comparing patterns. In data mining, there are algorithms, machine learning and database system which are necessary to conduct the data mining process.
Data mining parameters
In data mining, there are different parameters that are followed by the data mining process. There are different rules and regulations developed in order to analyse the data by using patterns, confidence and supports. Support in data mining is about the frequency of item appears in the database whereas confidence is a number of times are statement is correct or accurate. These are the necessary parameters of data mining process. including this, there are some other data mining parameters such as Sequence or Path analysis, classification, Clustering and Forecasting. These parameters in data mining help in evaluating the patterns of the database system. The sequence path in data mining is about the patterns' look in which the events are carried out one by one. The sequence path data mining technique is used by the retailer to check the buying habits of the customers.
Classification is generally based on machine learning, and it is a classic technique of data mining. In this technique, the data is distributed among the predefined set of classes in order to classify them in the correct manner. This technique is generally based on the mathematical techniques like a decision tree, linear programming, neural work and also statistics. In the classification technique of data mining, the software is developed which is programmed to classify the data items into classes. Clustering technique is another data mining technique that makes the useful cluster of the data or objects that repel similar characteristics using the automatic technique. The clustering technique is considered as one of powerful technique of data mining that helps in identifying the similar data from large datasets. In the clustering technique, the classes are defined, and the object is provided in each class. During the classification technique, the objects are assigned to the predefined classes. Through this, data is generally divided into different classes according to the similarity in between them.
Prediction technique in data mining is generally used to discover the relationship between the independent variables and dependent variables. The prediction technique is generally used in the sales department of the company to predict the profit for future. In this condition, the profit is based on the sales. Therefore, the sale is independent variable and profit is the dependent variable. By considering the sales and profit data from the past, the regression curve for the profit prediction can be developed with the help of this prediction technique.
Decision Tree data mining technique is most common techniques as it is one of the most easy data mining technique that can be understood or used by the users. In the decision making a tree, the root is considered as the question or conditions whereas the branches are considered as possible answers to the question or condition. Each branch or answer in the decision tree leads to another set of questions or conditions which further have answers. With this flow, the exact information can be extracted from the datasets.
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