Data Mining Functionalities

In this tutorial, we will learn about the various data mining functionalities. By IncludeHelp Last updated : April 17, 2023


To perceive the various form of patterns to be identified in data mining activities, the following functionalities should be considered. To define and find the kind of patterns to be discovered in data mining activities, data mining features are used. Data mining has a wide application for forecasting and characterizing data to big data.

The data mining tasks can be categories into two main categories -

  • Descriptive data mining:
    Descriptive data mining demonstrates the common characteristics in the results. It offers knowledge of the data and gives insight into what's going on inside the data without any prior idea. It includes any information to grasp what's going on in the data without a prior idea. Popular characteristics of the data are demonstrated in the data collection.
  • Predictive Data Mining:
    Predictive data mining provides prediction features from data to its users. For example -what will be the projection of the market analysis in the next quarter with the output of the previous quarters? In general, the predictive analysis forecasts or infers the features based on the data previously available.
    For example: Judging by the outcomes of medical exams of a patient who suffers from some diseases.

Data Mining Functionalities

1. Class/Concept Description: Characterization and Discrimination

It is important to link data with groups or related items. For example, computers and printers are types of goods for sale in the Hardware Shop.

  • Data Characterization: The characterization of data is a description of the key characteristics of objects in a target class which creates what is called a characteristic rule. To do this, a user can run a database query to compute the user-specified class through predefined modules to retrieve desired results from data at various abstraction levels.
    Eg;-Bar maps, curves, and pie charts,
  • Data Discrimination: Data discrimination creates a series of the rule called discriminate rules that are simply a distinction between the two classes aligned with the goal class and the opposite class of the general characteristics of objects.

2. Prediction

To distinguish the inaccessible data items, it uses regression analysis and detects the missing numeric values in the data. If the class mark is absent, classification is used to render the prediction. Due to its relevance in business intelligence, a prediction is common. There are two main methods of data prediction; the prediction of the classmark using the developed class model and the prediction of incomplete data using prediction analysis.

3. Classification

Classification is used to create data sets using predefined classes, as the model is used to classify new instances whose classification is not understood. The instances used to produce the model are known as data from preparation. For example - classify the employees on the basis of their salaries in a company.

4. Association Analysis

The link between the data and the rule that bind them is discovered. And two or more data attributes are associated. It associates qualities that are transacted together regularly. They work out what is called the rule of business that is commonly used in the study of stock market analysis.

5. Outlier Analysis

Data components that cannot be clustered into a given class or cluster are outliers. They are often referred to as data anomalies; in few instances, outliers can be called unwanted noise which needs to be discarded from the given data set, they may disclose some useful information in other areas, and hence can be treated as an important aspect and beneficial for the data analysis.

6. Cluster Analysis

Clustering is a process of arranging data in the data sets based on similarity features. Clustering is often called unsupervised classification since provided class labels do not execute the classification. There are some most relevant clustering methods which are based on the concept of maximizing the similarity between the objects of the same class and decreasing the similarity between objects in different classes.

7. Evolution & Deviation Analysis

We get clustering of data with evolution analysis. In this approach, a user can uncover hidden patterns from data sets. This analysis helps users to find features using similarities in patterns.

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