Data Mining is considered as an interdisciplinary field. It includes a set of various disciplines such as statistics, database systems, machine learning, visualization and information sciences.Classification of the data mining system helps users to understand the system and match their requirements with such systems.
Data mining systems can be categorized according to various criteria, as follows:
- Classification according to the application adapted:
This involves domain-specific application.For example, the data mining systems can be tailored accordingly for telecommunications, finance, stock markets, e-mails and so on.
- Classification according to the type of techniques utilized:
This technique involves the degree of user interaction or the technique of data analysis involved.For example, machine learning, visualization, pattern recognition, neural networks, database-oriented or data-warehouse oriented techniques.
- Classification according to the types of knowledge mined:
This is based on functionalities such as characterization, association, discrimination and correlation, prediction etc.
- Classification according to types of databases mined:
A database system can be classified as a ‘type of data’ or ‘use of data’ model or ‘application of data’.
- Basic Concept of Classification (Data Mining)
- Difference between Data Warehousing and Data Mining
- Types of Sources of Data in Data Mining
- Data Mining: Data Warehouse Process
- Data Mining: Data Attributes and Quality
- Data Transformation in Data Mining
- Data Normalization in Data Mining
- Data Reduction in Data Mining
- Data Preprocessing in Data Mining
- Data Integration in Data Mining
- Data Mining
- Data Mining | Set 2
- Binning in Data Mining
- Challenges of Data Mining
- KDD Process in Data Mining
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