27 Jun 2016 Data mining & Business Intelligence
The term Data mining refers to one of the processes involved in the task of extracting knowledge from a database, also known as KDD (Knowledge Discovery in databases).
However, by extension, data mining is referred to as the KDD global process because of its commercial appeal.
Understanding data mining as a KDD sub-process, we could define the term as the process of extracting underlying knowledge from a large volume of data.
Data mining is a recent development directly linked to the scientific fields of mathematics (mainly statistics), computer science and artificial intelligence. Data mining can be supported by different Business Intelligence systems, from which we can obtain several advantages.
Data mining is the way to extract unknown knowledge from data. It works well in tandem with Business intelligence systems (OLAP), as we can use a data warehouse to easily access
the datasets, as the information is already integrated and clean. So, if we use a data warehouse from our BI system, we can skip phases I and II (information integration and cleansing), some of the most time-consuming tasks.
In this article we´re highlighting how a Business Intelligence system is a great starting point for the data mining process and how data mining can be used for process optimization.
Topics covered:
- Definition
- Objectives and challenges➀ The challenge: working with large datasets➁ The objective: get knowledge from data
- KDD Process Phases
- Applications:
➀ Data mining & Business Intelligence
➁ Extracting knowledge from unstructured data
➂ Optimization of processes
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