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Data Mining is the process of identifying or searching the particular data which are stored in larger amount of database. It helps to discover the hidden patterns, relationship and the future trends automatically. Data Mining is the process of identifying or searching the particular data which are stored in the large amount of database. It helps to discover the hidden patterns, relationship, and the future trends automatically. Data Mining is also known as Knowledge Discovery in Data (KDD), Knowledge mining from data, Knowledge Extraction, Data Dredging, Data Analysis, Pattern Analysis, and Data Archaeology.
The primary aim of the data mining is to gather the details about a data from the data set and convert it into the easiest format which is understandable to everyone for future usage. Just go through the task involved in Data Mining,
Discovering the pattern in which one event is associated with another event. This method of a connected event is referred as depending modeling that identifies the relationship between the variables.
At the beginning of the analysis, clustering analysis the data without the class labels. Clustering is used to generate class labels for a data group.
Classification is the process, making use of which the function of data was found which describes and distinguish data concepts and data classes.
This analysis is mostly used for the mathematical representation such as numeric prediction, statistical methodology. It also discovers the distribution of data.
It is also known as sequential path analysis, this is why because it this analysis describes the path or pattern where event leads another event.
The Outlier is the data objects that do not have any of the models and the behavior of a data. The Outlier analysis detects the fraud data present in the data set. This outlier analysis is otherwise called as Anomaly mining.