Data Mining Applications

Data mining is a result of the natural evolution of Information Technology and is used to convert raw data into useful information. It is a multidimensional database approach which uses machine learning, data visualization, statistics, database technology, pattern recognition, neural networks, information retrieval and soft computing etc. Data Mining is so much concerned about customer experiences and user interface in different sectors like communication, marketing organizations, financial and banking sectors, retails etc in order to get an idea about pricing, impact of sales, customer preferences, corporate profits, customer satisfaction and product positioning. 

Data mining is widely used to find the real cause of  network attack, fraud detection and anomalies. Data mining techniques are proven useful in selecting and structuring most relevant information from a repository of data sets. In order to proceed market analysis, finding manufacturing problems, acquire new customers, accurately profile customers and in identifying new products sources we would require data mining technologies.

Banking and finance sector uses data mining to better understand market risks. It is widely applied to credibility ratings, play important role in card transactions, to detect and analyse fraud, keep a track of customer financial data and to trace purchasing patterns. While analysing large amount of data, businesses can learn more about customer preferences and help to program effective marketing strategies, increases sales and decrease costs.

Below are few Data Mining Techniques :

  • Classification Analysis
  • Association Rule Learning
  • Anomaly or Outlier Detection
  • Clustering Analysis
  • Regression Analysis
Data Mining : A Database Blue Print
Data Mining : A Database Blue Print

KDD (Knowledge Discovery in Database) Data Mining Process fetches useful data and applies them to specific algorithms for extracting similar patterns from data to form insights relevant to their business needs. It can be achieved by using mathematical analysis to drive patterns and trends that are available within the data and are defined as data mining models opted for business intelligence and data science.

Limitations of Data Mining :

  • Accuracy
  • Technical Skills
  • Cost
  • Information Misuse
  • Additional Information
  • Privacy
  • Security

Data Mining Tools/frameworks :

There are certain frameworks that are used to detect the patterns, trends, groupings amongst large data sets and transforming data into more refined information. The frameworks are Rstudio, Tableau used for data mining analysis. Based on the analytical process, algorithms and models data mining can be done in different steps or phases for example : CIA Intelligence Process and CRISP – DM Process Model used for data and business  understanding, data preparation, modelling, evaluation and deployment. From a set of junk databases discovering some patterns and combining them to provide valuable insights is the popular aspect of data mining. Data mining can be done by using R and Python languages. 

Cutting edge programming languages :

  • Python
  • R and SQL
  • Quantitative Modelling
  • Big Data for Business
  • Artificial Intelligence
  • Advanced Marketing Analytics
  • Infrastructure Management

It assists organizations to gather data. It helps data scientists effectively break down huge measures of information rapidly. Data Scientists can utilize the data to distinguish extortion, construct hazard models, and further develop product security. A database is any assortment of information coordinated for capacity, availability, and recovery. A data warehouse is a sort of data set that incorporates duplicates of exchange information from unique source frameworks and arranges them for scientific use.

Disciplines that uses Data Mining :

  • Market Analysis
  • Healthcare
  • Education
  • Engineering
  • Intrusion Detection
  • Forensics
  • Customer Segmentation
  • Financial Banking
  • Research
  • Bioinformatics