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What are the techniques of data mining?
Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.
The major challenge is to analyze the data to extract essential data that can be used to solve an issue or for company development. There are many dynamic instruments and techniques available to mine data and discover better judgment from it.
There are various techniques of data mining which are as follows −
Classification − Classification is a data-mining technique that creates elements to a set of data to aid in more efficient predictions and analysis. There are several methods intended to create the analysis of very huge datasets effective.
Classification is one of the most important tasks in data mining. It refers to a process of assigning pre-defined class labels to instances based on their attributes. There is a similarity among classification and clustering, it looks same, but it is different. The major difference between classification and clustering is that classification involves the leveling of elements as per their membership in pre-defined groups.
Clustering − The phase of combining a set of physical or abstract objects into classes of the similar objects is referred to as clustering. A cluster is a set of data objects that are the same as one another within the same cluster and are disparate from the objects in other clusters. A cluster of data objects can be considered collectively as one group in several applications. Cluster analysis is an essential human activity.
Regression −These approaches are used to forecast the value of a response (dependent) variable from one or more predictor (independent) variables where the variables are numeric. There are several forms of regression, including linear, multiple, weighted, polynomial, nonparametric, and robust (robust techniques are beneficial when errors fail to satisfy normalcy conditions or when the data includes significant outliers).
Outer detection − This type of data mining technique relates to the observation of data items in the data set, which do not match an expected pattern or expected behavior. This technique may be used in various domains like an intrusion, detection, fraud detection, etc. It is also known as Outlier Analysis or Outlier mining.
Sequential Patterns − The sequential pattern is a data mining technique specialized for computing sequential data to find sequential patterns. It includes finding interesting subsequences in a collection of sequences, where the stake of a sequence can be measured in terms of several element like length, occurrence frequency, etc.
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