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Data Mining  

Data mining, also called knowledge discovery in database (KDD), is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data in database. There have been many advances on researches and developments of data mining, and many data mining techniques and systems have recently been developed. Data mining is finding increasing acceptance in science and business areas which need to analyze large amounts of data to discover trends which they could not otherwise find.

Data mining is the technique and applcation of the union of developments in statistics, AI, and machine learning for data analysis and finding previously-hidden trends or patterns within large amounts of data.

Data mining techniques are the result of a long process of research and development. This evolution began with data collection (1960s) on computers, continued with improvements in data access(1980s), and then Data Warehousing & Decision Support (1990s), and more recently, generated technologies that allow users to automatically, intelligently and rapidly manage data, analyze and extract implicit, previously unknown interesting data patterns, relationships and knowledge that hide within the data in real time.

 
   
Data mining tasks  
  • Model Building : aim to build explicit models
    • Classification
    • Prediction
  • Automatic Pattern Extraction : aim to automatically identify useful patterns in data
    • Cluster Analysis
    • Association rule mining
  • Interactive Visual Data Exploration: aim to simply help to describe complex information and better understand what is going on in the data
    • visualization of data
    • visual data exploration
 
   
Important base of techniques  

Machine Learning
The one of the most important base of techniques for data mining is machine learning, which is more accurately described as the union of statistics and AI. Machine learning could be considered an evolution of AI, because it blends AI heuristics with advanced statistical analysis. Machine learning attempts to let computer programs learn about the data they study, such that programs make different decisions based on the qualities of the studied data, using statistics for fundamental concepts, and adding more advanced AI heuristics and algorithms to achieve its goals. Data mining, in many ways, is fundamentally the adaptation of machine learning techniques to business applications.

Many techniques are used in data mining :

  • Fuzzy set
  • Rough set
  • Concept lattice
  • Decision trees
  • Genetic algorithms
  • Bayesian network
  • Nneural networks
  • Nearest neighbor method
  • SVM(Support Vector Machines)
  • Bagging (Voting, Averaging)
  • Boosting
  • Rule induction
 
   
The processes of Data mining and KDD  
  • Preprocessing - this is the data cleansing stage where certain information is removed which is deemed unnecessary and may slow down queries for example unnecessary to note the sex of a patient when studying pregnancy. Also the data is reconfigured to ensure a consistent format as there is a possibility of inconsistent formats because the data is drawn from several sources e.g. sex may recorded as f or m and also as 1 or 0.
  • Selection - selecting or segmenting the data according to some criteria
  • Transformation - the data is not merely transferred across but transformed in that overlays may added such as the demographic overlays commonly used in market research. The data is made useable and navigable.
  • Data mining - extraction of patterns from the data. This is core of KDD.
  • Interpretation and evaluation - the patterns identified by the system are interpreted into knowledge which can then be used to support human decision-making e.g. prediction and classification tasks, summarizing the contents of a database or explaining observed phenomena.
 
 
   
References  
[1] R. Cooley , B. Mobasher , J. Srivastava, Web mining: Information and pattern discovery on the World Wide Web, Proceedings of the International Conference on Tools with Artificial Intelligence, 1997.
[2] Myra Spilioupulou, Laborious way from data mining to web log mining, Computer Systems Science and Engineering, Vol. 14, No. 2, 1999, p113-125.
[3] Maurizio Cibelli, Gennaro Costagliola, Automatic generation of Web mining environments, Proceedings of SPIE, The International Society for Optical Engineering. Vol. 3695, 1999, p215-225.
[4] Osmar R. Zaiane , Man Xin , Jiawei Han , Discovering web access patterns and trends by applying OLAP and data mining technology on web logs, Proceedings of the Forum on Research and Technology Advances in Digital Libraries, ADL, 1998.
[5] Minos et al. Sequential pattern mining with regular expression constraints. VLDB 1999.
[6] Rakesh Agrawal, Ramakrishnam Srikant, Mining sequential patterns. ICDE 1995.
[7] T. Imielinski, A. Virmani, MSQL: A Query Language for Database Mining, Data Mining and Knowledge Discovery, Vol. 3, 1999.
[8] J. Han. Data mining techniques. SIGMOD, 1996.
[9] M. Garofalakis, R. Rastogi, S. Seshadri, and K, Shim. Data Mining and the Web: Past, Present, and Future
[10] Chaudhuri, Surajit, Umeshwar Dayal, "An Overview of Data Warehousing and OLAP Technology". SIGMOD Record, Vol. 26, No. 1, March 1997
[11] H. Chipman, E. I. George, and R. E. McCulloch. Bayesian CART model search (with discussion). Journal of the American Statistical Association, 93:935-960, 1998.
[12] J. Han, Y. Cai, and N. Cercone, Knowledge Discovery in Databases: An Attribute-Oriented Approach", VLDB-92, Vancouver, British Columbia, Canada, 1992, 547-559.
 
 

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