Data Mining And Its Techniques

Data Mining and its Techniques: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that has the most direct business applications.
This overview provides a description of some of the most common data mining algorithms in use today. We have broken the discussion into two sections, each with a specific theme:
Classical Techniques: Statistics, Neighborhoods and Clustering
Next Generation Techniques: Trees, Networks and Rules
Each section will describe a number of data mining algorithms at a high level, focusing on the "big picture" so that the reader will be able to understand how each algorithm fits into the landscape of data mining techniques.
Cluster analysis is an important data mining technique used to find data segmentation and pattern information. By clustering the data, people can obtain the data distribution, observe the character of each cluster, and make further study on particular clusters. In addition, cluster analysis usually acts as the preprocessing of other data mining operations. In the procedure of Correspondence Analysis Clustering Method, we construct a standardized factor component matrix, resolve the factor score in correspondence analysis, and for the first time introduce factor rotation into correspondence analysis. All of above work expand to some extent the methodology and theory system of correspondence analysis
Association rule learning Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
Clustering is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
Classification is the task of generalizing known structure to apply to new data. For example, an email program might attempt to classify an email as legitimate or spam. Common algorithms include decision tree learning, nearest neighbor, naive Bayesian classification, neural networks and support vector machines.
Regression Attempts to find a function which models the data with the least error.