Data mining is the process of identifying patterns and extracting information from big data sets using techniques that combine machine learning, statistics, and database systems.
With the overarching objective of extracting information (using intelligent methods) from a data set and structuring the information into an intelligible form for subsequent use, data mining is an interdisciplinary branch of computer science and statistics.
The analytical phase of the knowledge discovery in databases (KDD) process is called data mining.
Along with the raw analysis stage, other components of the process include database and data management, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of found structures, visualization, and online updating.
Data Mining Features:
- Sort through the inconsistent and recurring noise in your data.
- Allows for knowing what is pertinent and then effectively using that knowledge to predict outcomes.
- Increase the rate at which you make intelligent choices.
Typical Data Mining System Architecture:
Data Mining Types:
Predictive Data Mining Analysis: Analysis using predictive data mining is done on data that can be used to predict future events in the business world. The following four categories further break down predictive data mining:
- Analysis of Classifications
- Analysis of Regression
- Time-Intensive / Time Serious Analysis
- Analysis of Prediction
Descriptive Data Mining Analysis: The goal of descriptive data mining is to summarize or transform inputted data into pertinent knowledge. The following four types can be used to further categorize the descriptive data-mining tasks:
- Clustering Analysis
- Summarization Analysis
- Association Rules Analysis
- Sequence Discovery Analysis
Hope you find this article informative.