Data Mining Definition, Types, Importance and Tools Explained
Data mining is an approach to big data processing that uses predictive and descriptive techniques to analyze and extract usable information from raw datasets.
This article discusses data mining definition, types, importance and tools, as follows;
-Data Mining Definition: 6 Ways to Define Data Mining
Data Mining Definition: 6 Ways to Define Data Mining
Data mining is the act and process of sorting through large sets of data to recognize trends that can be used understand the datasets, as well as to solve real-life problems .
The above data mining definition highlights the recognition of trends. This is very similar to what is done in machine learning, which is also the essence of neural networks that cumulate large amounts of data in a classifiable manner.
Below is an alternative data mining definition, that further clarifies the relationship between data mining, machine learning, and artificial intelligence technology;
Data mining is the extraction of implicit information from data, which can be performed as part of knowledge discovery in artificial intelligence, in the process of deep learning from large databases .
Although the term 'machine learning' is not directly referenced above, it is acknowledged that data mining can in fact be performed by algorithms in the process of deep learning (which is a variant form of machine learning) in AI systems.
The information that is derived through data mining is often useful to solve problems and improve the performance of data-dependent systems. Below is an alternative data mining definition that highlights some uses of data mining;
Data mining is the assessment and manipulation of large amounts of data to glean information that can be used in health diagnosis, fraud detection, database integration, market analysis, environmental remediation and monitoring, among other applications.
Understanding the meaning of data mining can be helped by gaining a picture of how the process works. The following data mining definition outlines steps in the data mining process;
Data mining is the use of specialized algorithms to extract potentially useful information from large blocks of raw data through a process that comprises of steps like; data acquisition, integration, processing (analysis, reduction, transformation) pattern recognition (modeling) and knowledge representation.
Another factor that is important toward outlining the data mining definition, is the method(s) of data mining. Basically, these methods are identified by the categorization of data mining techniques into groups that share similarity in approach and/or potential outcome.
Below is the definition of data mining based on methods;
Data mining is the practice whereby massive data volumes are processed to yield usable information using any of various methods or approaches that include; detective, predictive, descriptive, integrative, and cumulative data mining, respectively.
Lastly, the data mining definition is given in such a manner that outlines the techniques used in data mining;
Data mining is the use of techniques like; classification, regression, association, clustering, and prediction, to process large amounts of data and extract meaningful information that can be applied toward problem-solving .
Types of Data Mining
Types of data mining based on method or approach are;
1). Predictive Data Mining (as one of the Types of Data Mining)
Predictive data mining is a type of data mining approach that uses intensive analysis of current and historical datasets to predict future occurrences.
As the name implies, this type of data mining relies on the ability of specialized algorithms to carry out predictions of events.
An example of prediction in data mining is the analysis of data to interpret symptoms of ailments in healthcare. The data collected from past organ-functioning conditions in this case can be cumulated and analyzed to extract meaningful information which could point toward possible future health conditions.
2). Descriptive Data Mining (as one of the Types of Data Mining)
Descriptive data mining is a type of mining that aims to classify and characterize datasets as a way of extracting meaningful information that can be used to solve a problem.
The difference between descriptive and predictive data mining is in their purpose; where descriptive mining aims to characterize and understand datasets while predictive mining aims to extract information that can be used to spot trends and predict future outcomes .
Both predictive and descriptive data mining approaches are useful in many scenarios, so that they may be applied simultaneously or sequentially to a given dataset to achieve both prediction and characterization respectively .
Descriptive mining methods include; cross-tabulation, frequency assessment, comparative compilation and correlation.
Importance of Data Mining
The main purpose of data mining is to transform large volumes of raw data to understandable and applicable information, either by characterization, or predictive analysis.
The importance of data mining includes its role in;
1). Large-scale data collection
2). Data security maintenance
3). Operation of AI systems
4). Smart device integration in IoT frameworks
5). Knowledge-based problem solving
7). Multi-attribute characterization of data
Data Mining Tools
Data mining tools are algorithms that are designed to carry out functions like regressive analysis and cross tabulation, which help to extract information from large datasets.
Types of data mining tools are; predictive and descriptive algorithms, although many modern software for data mining are equipped to serve as both types.
Some examples of data mining tools are;
2). SAS Data Mining
3). IBM SPSS Modeler
6). Apache Mahout
7). Rapid Miner
Data mining is the extraction of useful information from enormous amounts of cumulated data, through predictive and/or descriptive analyses.
Types of data mining are;
1. Predictive Data Mining
2. Descriptive Data Mining
The importance of data mining is based on its role in; large-scale data collection, data security maintenance, operation of AI systems, smart device integration in IoT frameworks, knowledge-based problem solving, forecasting, and multi-attribute characterization of data.
Some data mining tools are; Rattle, SAS Data Mining, IBM SPSS Modeler, Orange, KNIME, Apache Mahout, Rapid Miner, Sisense, SSDT, Dundad, Weka, Inetsoft, and R-Programming.
1). Al-Falah, M. M. (2022). "Difference Between Descriptive and Predictive Data Mining." Available at: https://www.researchgate.net/publication/364055134_Difference_Between_Descriptive_and_Predictive_Data_Mining. (Accessed 19 March 2023).
2). Ganesh, C.; Reddy, E. K. (2022). "Overview of the Predictive Data Mining Techniques." INTERNATIONAL JOURNAL OF COMPUTER SCIENCES AND ENGINEERING 10(1):30. Available at: https://doi.org/10.26438/ijcse/v10i1.2836. (Accessed 19 March 2023).
3). Jassim, M. A.; Abdulwahid, S. N. (2021). "Data Mining preparation: Process, Techniques and Major Issues in Data Analysis." IOP Conference Series Materials Science and Engineering 1090(1):012053. Available at: https://doi.org/10.1088/1757-899X/1090/1/012053. (Accessed 20 March 2023).
4). Leopord, H.; Cheruiyot, W.; Kimani, S. (2016). "A Survey and Analysis on Classification and Regression Data Mining Techniques for Diseases Outbreak Prediction in Datasets." Available at: https://www.researchgate.net/publication/317011667_A_Survey_and_Analysis_on_Classification_and_Regression_Data_Mining_Techniques_for_Diseases_Outbreak_Prediction_in_Datasets.(Accessed 20 March 2023).
5). Zia, A.; Aziz, M.; Popa, I.; Khan, S. A.; Hamedani, A. F.; Asif, A. R. (2022). "Artificial Intelligence-Based Medical Data Mining." J Pers Med. 2022 Aug 24;12(9):1359. Available at: https://doi.org/10.3390/jpm12091359. (Accessed 20 March 2023).