Data mining, sometimes called data or knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information – information that can be used to increase revenue, cuts costs, or both.

Data mining is primarily used today by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics..

Data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks.

There are about five elements of data mining:

  • Extract, transform, and load transaction data onto the data warehouse system.
  • Store and manage the data in a multidimensional database system.
  • Provide data access to business analysts and information technology professionals.
  • Analyze the data by application software.
  • Present the data in a useful format, such as a graph or table.

There are several major data mining techniques have been developing and using in data mining projects recently including:

  • Association
  • Clustering
  • Classification
  • Prediction
  • Sequential patterns
  • Decision trees

Association is one of the best known data mining technique. In association, a pattern is discovered based on a relationship between items in the same transaction. That’s is the reason why association technique is also known as relation technique. The association technique is used in market basket analysis to identify a set of products that customers frequently purchase together.

Clustering is a data mining technique that makes meaningful or useful cluster of objects which have similar characteristics using automatic technique. The clustering technique defines the classes and puts objects in each class, while in the classification techniques, objects are assigned into predefined classes. To make the concept clearer, we can take book management in library as an example. In a library, there is a wide range of books in various topics available.

Classification is a classic data mining technique based on machine learning. Basically classification is used to classify each item in a set of data into one of predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics. In classification, we develop the software that can learn how to classify the data items into groups.

The prediction, as it name implied, is one of a data mining techniques that discovers relationship between independent variables and relationship between dependent and independent variables. For instance, the prediction analysis technique can be used in sale to predict profit for the future if we consider sale is an independent variable, profit could be a dependent variable. Then based on the historical sale and profit data, we can draw a fitted regression curve that is used for profit prediction.

Sequential patterns analysis is one of data mining technique that seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period.

Decision tree is one of the most used data mining techniques because its model is easy to understand for users. In decision tree technique, the root of the decision tree is a simple question or condition that has multiple answers.

Data mining is not an easy task, as the algorithms used can get very complex and data is not always available at one place. It needs to be integrated from various heterogeneous data sources. These factors also create some issues. Here in this tutorial.

Data mining systems depend on data base to supply the raw input and this raises problems, such as that database tend to be dynamic, incomplete, noisy and large . Other problems arises as a result f the inadequacy and irrelevance of the information stored.

The main difficulties of data mining are:

  • Limited information
  • Noise or missing data
  • User interaction and prior knowledge
  • Uncertainty
  • Size, updates and irrelevant fields