Data Mining Concepts and Applications

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

It is a process used to extract usable data from a larger set of any raw data. It implies analyzing data patterns in large batches of data using one or more software. Itinvolves effective data collection and warehousing as well as computer processing. For segmenting the data and evaluating the probability of future events, data mining uses sophisticated mathematical algorithms. It also known as Knowledge Discovery in Data (KDD).

Actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records , unusual records, and dependencies. This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.

Data Mining Applications

Data Mining in Finance

  • We have to Increase customer loyalty by collecting and analyzing customer behavior data. Also, one needs to help banks that predict customer behavior and launch relevant services and products.
  • Helps in Discovering hidden correlations between various financial indicators that need to detect suspicious activities with a high potential risk.
  • Generally, it identifies fraudulent or non-fraudulent actions. As it done by collecting historical data. And then turning it into valid and useful information.

Data Mining in Healthcare

  • Basically, it provides government, regulatory and competitor information that can fuel competitive advantage. Although, it supports the R&D process. And then go-to-market strategy with rapid access to information at every phase.
  • Generally, it discovers the relationships between diseases and the effectiveness of treatments. That is to identify new drugs or to ensure that patients receive appropriate, timely care.
  • Also, it supports healthcare insurers in detecting fraud and abuse.

Data mining for Intelligence

  • Generally, it reveals hidden data related to money laundering, narcotics trafficking, etc.
  • Also, helps in Improving intrusion detection with a high focus on anomaly detection. And identify suspicious activity from a day one.
  • Basically, convert text-based crime reports into word processing files. That can be used to support the crime-matching process.

Data mining in Telecommunication

  • In this, data mining gains a competitive advantage and reduce customer churn by understanding demographic characteristics and predicting customer behavior.
  • Increases customer loyalty and improve profitability by providing customized services.
  • As it supports customer strategy by developing appropriate marketing campaigns and pricing strategies.


Many E-commerce companies are using data mining business Intelligence to offer cross-sells through their websites. One of the most famous of these is, of course, Amazon. They use sophisticated mining techniques to drive their ‘People who viewed that product. Also liked this’ functionality.

Crime Agencies

Beyond corporate applications, crime prevention agencies use analytics. And Data Mining to spot trends across myriads of data. That should help with everything from where to deploy police manpower. And Particularly who to search at a border crossing. And even which intelligence to take seriously in counter-terrorism activities.


There is a newly emerging field, called Educational Data Mining. As it concerns with developing methods. That discover knowledge from data originating from educational Environments. The goals of EDM are identified as predicting students’ future learning behavior, studying. We use data mining by an institution to take accurate decisions. And also to predict the results of the student. With the results, the institution can focus on what to teach and how to teach. Learning pattern of the students can be captured. And used to develop techniques to teach them.