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Data mining is an important aspect of knowledge discovery in the field of computer science that aims to extract meaningful patterns, hidden predictive information and insights from large datasets. It is a powerful technology used widely in diverse areas, to predict future trends and behaviors, to make proactive knowledge-driven decisions for organizations and help store the most important information in their data warehouses.
Data mining techniques are used to analyze and transform raw data into actionable knowledge, which can be used to make informed business decisions, improve products and services, and enhance customer experiences.
Organizations use various commercial Data Mining Systems available today to identify hidden patterns and relationships within data that may not be apparent through manual analysis. This can lead to the discovery of new trends, opportunities, and insights that can help organizations optimize their operations, improve their competitiveness, and make data-driven decisions.
Classification of Data Mining Systems
Data mining systems can be classified according to various criteria as follows:
1. According to the type of data source mined
This classification is based on the type of data handled such as spatial data, multimedia data, time-series data, text data, World Wide Web, etc.
2. According to the data model
This classification is based on the data model involved such as relational database, object-oriented database, data warehouse, transactional database, etc.
3. According to the kind of knowledge discovered
This classification is based on the kind of knowledge discovered or data mining functionalities, such as characterization, discrimination, association, classification, clustering, etc. Some systems tend to be comprehensive systems offering several data mining functionalities together.
4. According to mining techniques used
This classification is based on the data analysis approach used such as machine learning, neural networks, genetic algorithms, statistics, visualization, database oriented or data warehouse-oriented, etc. The classification can also take into account the degree of user interaction involved in the data mining process such as query-driven systems, interactive exploratory systems, or autonomous systems. A comprehensive system would provide a wide variety of data mining techniques to fit different situations and options, and offer different degrees of user interaction.
Choosing a Data Mining System
While selecting a suitable data mining system for your requirements, you should consider below factors:
Type of data - Different data mining systems are designed to handle different types of data, such as structured data, unstructured data, and semi-structured data. It may handle formatted text, record-based data, and relational data. The data could also be in ASCII text, relational database data or data warehouse data. It is important to consider the type of data that needs to be analyzed and ensure that the chosen system is capable of handling that type of data. Therefore, we should check what exact format the data mining system can handle.
Type of data sources - Data mining systems can be used to analyze data from various sources, such as databases, text documents, and social media. Data sources refer to the data formats in which data mining system will operate. Some data mining system may work only on ASCII text files while others on multiple relational sources. Data mining system should also support ODBC (Open Database Connectivity) connections or OLE DB (Object Linking and Embedding Database) connections. It is important to consider the data sources and ensure that the chosen system is compatible with those sources.
System issues - We must consider the compatibility of a data mining system with different operating systems. It is important to consider the system issues, such as hardware requirements, software compatibility, and system reliability. One data mining system may run on only one operating system or on several. There are also data mining systems that provide web-based user interfaces and allow XML data as input. The chosen system should be able to operate within the existing infrastructure and meet the organization's specific requirements.
Data mining methods - Different data mining systems use different methods for analyzing data. There are some data mining systems that provide only one data mining function such as classification while some provides multiple data mining functions such as Concept description, Discovery-driven OLAP analysis, Association rule mining, Linkage analysis, Statistical analysis, Prediction, Clustering, Outlier analysis, Similarity search, etc. It is important to consider the specific data mining methods required for the analysis and ensure that the chosen system can implement those methods.
Coupling data mining with databases or data warehouse systems − Data mining systems need to be coupled with a database or a data warehouse system, when integrating with existing databases, data warehouses, or data marts. It is important to consider the database integration capabilities of the chosen system and ensure that it is compatible with the existing infrastructure. The coupled components are integrated into a uniform information processing environment. Here are the types of coupling listed below −
a) No Coupling b) Loose Coupling c) Semi tight Coupling d) Tight Coupling
Scalability - It is important to consider the system's scalability, which refers to the ability to handle increasing amounts of data and users. The chosen system should be able to scale up or down based on the organization's changing needs. There are two scalability issues in data mining −
a) Row (Database size) Scalability − A data mining system is considered as row scalable when the number or rows are enlarged 10 times. It takes no more than 10 times to execute a query
b) Column (Dimension) Scalability − A data mining system is considered as column scalable if the mining query execution time increases linearly with the number of columns.
Visualization - Data mining systems should be able to present the analyzed data in a clear and understandable format, such as charts, graphs, and reports. It is important to consider the visualization capabilities of the chosen system and ensure that it meets the specific needs of the organization. Visualization in data mining can be categorized as follows − Data Visualization, Mining Results Visualization, Mining process visualization, Visual data mining
Data Mining query language and graphical user interface − The chosen data mining system should have an easy-to-use graphical user interface that allows users to interact with the system easily. It is important to consider the user interface design and ensure that it is intuitive and easy to use for the intended users. It is important to promote user-guided, interactive data mining. Unlike relational database systems, data mining systems do not share underlying data mining query language.
Trends in Data Mining
Data mining concepts are still evolving. Below are some of the latest trends and technology used in the field of data mining -
Application Exploration
Scalable and interactive data mining methods
Integration of data mining with database systems, data warehouse systems and web database systems
Standardization of data mining query language
Visual data mining
Research Analysis
New methods for mining complex types of data
Biological data mining
Data mining and software engineering
Web mining
Distributed data mining
Real time data mining
Multi database data mining
Privacy protection and information security in data mining
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