Predictive analytics is currently one of the most important Big Data trends. But both predictive analytics and data mining attempt to make predictions about possible events in the future with the help of data models. What are the differences between them?
“Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.” (Wikipedia)
The classical data mining techniques include:
- Data clustering – The aim here is to segment data and to form various groups
- Data classification – Data elements are automatically assigned to different predefined groups/classes
- Regression analysis – relations between (more) dependent and independent variables are identified
- Association analysis – search for patterns in which an event is connected to another event; the dependencies between the data sets are described on if-then rules.
“Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.” (Wikipedia)
This article originally appeared on ecmapping.com. To read the full article, click here.