Data mining

From Wikipedia, the free encyclopedia

Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods. It has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data"[1] and "the science of extracting useful information from large data sets or databases."[2] Data mining in relation to enterprise resource planning is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making.[3]

Contents

[edit] Background

Traditionally, analysts have performed the task of extracting useful information from recorded data, but the increasing volume of data in modern business and science calls for computer-based approaches. As data sets have grown in size and complexity, there has been a shift away from direct hands-on data analysis toward indirect, automatic data analysis using more complex and sophisticated tools. The modern technologies of computers, networks, and sensors have made data collection and organization much easier. However, the captured data needs to be converted into information and knowledge to become useful. Data mining is the entire process of applying computer-based methodology, including new techniques for knowledge discovery, from data.[4]

Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, users have the ability to identify key attributes of business processes and target opportunities.

Although data mining is a relatively new term, the technology is not. Companies for a long time have used powerful computers to sift through volumes of data such as supermarket scanner data to produce market research reports. Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of analysis.

The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user. Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g. rule based systems) and opaque in others such as neural networks. Moreover, some data-mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery.

Metadata, or data about a given data set, are often expressed in a condensed data-minable format, or one that facilitates the practice of data mining. Common examples include executive summaries and scientific abstracts.

Data mining relies on the use of real world data. This data is extremely vulnerable to collinearity precisely because data from the real world may have unknown interrelations. An unavoidable weakness of data mining is that the critical data that may explain the relationships is never observed. Alternative approaches using an experiment based approach such as Choice Modelling for human-generated data may be used. Inherent correlations are either controlled for or removed altogether through the construction of an experimental design.

Recently, there were some efforts to define a standard for data mining, for example the CRISP-DM standard for analysis processes or the Java Data-Mining Standard. Independent of these standardization efforts, freely available open-source software systems like RapidMiner and Weka have become an informal standard for defining data-mining processes.

[edit] Privacy concerns

There are also privacy and human rights concerns associated with data mining, specifically regarding the source of the data analyzed. Data mining provides information that may be difficult to obtain otherwise. When the data collected involves individual people, there are many questions concerning privacy, legality, and ethics.[5] In particular, data mining government or commercial data sets for national security or law enforcement purposes has raised privacy concerns.[6][7]

[edit] Notable uses of data mining

[edit] Terrorism

Data mining has been cited as the method by which the U.S. Army unit Able Danger had identified the September 11, 2001 attacks leader, Mohamed Atta, and three other 9/11 hijackers as possible members of an Al Qaeda cell operating in the U.S. more than a year before the attack.

It has been suggested that both the Central Intelligence Agency and the Canadian Security Intelligence Service have employed this method.[8]

Previous data mining to stop terrorist programs under the US government include the Terrorism Information Awareness (TIA) program, Computer-Assisted Passenger Prescreening System (CAPPS II), Analysis, Dissemination, Visualization, Insight, and Semantic Enhancement (ADVISE), Multistate Anti-Terrorism Information Exchange (MATRIX), and the Secure Flight program Security-MSNBC. These programs have been discontinued due to controversy over whether they violate the US Constitution's 4th amendment.

[edit] Games

Since the early 1960s, with the availability of oracles for certain combinatorial games, also called tablebases (e.g. for 3x3-chess) with any beginning configuration, small-board dots-and-boxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened up. This is the extraction of human-usable strategies from these oracles. Current pattern recognition approaches do not seem to fully have the required high level of abstraction in order to be applied successfully. Instead, extensive experimentation with the tablebases combined with an intensive study of tablebase-answers to well designed problems and with knowledge of prior art i.e. pre-tablebase knowledge is used to yield insightful patterns. Berlekamp in dots-and-boxes etc. and John Nunn in chess endgames are notable examples of researchers doing this work, though they were not and are not involved in tablebase generation.

[edit] Business

Data mining in customer relationship management applications can contribute significantly to the bottom line. Rather than contacting a prospect or customer through a call center or sending mail, only prospects that are predicted to have a high likelihood of responding to an offer are contacted. More sophisticated methods may be used to optimize across campaigns so that we can predict which channel and which offer an individual is most likely to respond to - across all potential offers. Finally, in cases where many people will take an action without an offer, uplift modeling can be used to determine which people will have the greatest increase in responding if given an offer. Data clustering can also be used to automatically discover the segments or groups within a customer data set.

Businesses employing data mining quickly see a return on investment, but also they recognize that the number of predictive models can quickly become very large. Rather than one model to predict which customers will churn, a business could build a separate model for each region and customer type. Then instead of sending an offer to all people that are likely to churn, it may only want to send offers to customers that will likely take to offer. And finally, it may also want to determine which customers are going to be profitable over a window of time and only send the offers to those that are likely to be profitable. In order to maintain this quantity of models, they need to manage model versions and move to automated data mining.

Data mining can also be helpful to human-resources departments in identifying the characteristics of their most successful employees. Information obtained, such as universities attended by highly successful employees, can help HR focus recruiting efforts accordingly. Additionally, Strategic Enterprise Management applications help a company translate corporate-level goals, such as profit and margin share targets, into operational decisions, such as production plans and workforce levels.[9]

Another example of data mining, often called the market basket analysis, relates to its use in retail sales. If a clothing store records the purchases of customers, a data-mining system could identify those customers who favour silk shirts over cotton ones. Although some explanations of relationships may be difficult, taking advantage of it is easier. The example deals with association rules within transaction-based data. Not all data are transaction based and logical or inexact rules may also be present within a database. In a manufacturing application, an inexact rule may state that 73% of products which have a specific defect or problem, will develop a secondary problem within the next six months.

Related to an integrated-circuit production line, an example of data mining is described in the paper "Mining IC Test Data to Optimize VLSI Testing."[10] In this paper the application of data mining and decision analysis to the problem of die-level functional test is described. Experiments mentioned in this paper demonstrate the ability of applying a system of mining historical die-test data to create a probabilistic model of patterns of die failure which are then utilized to decide in real time which die to test next and when to stop testing. This system has been shown, based on experiments with historical test data, to have the potential to improve profits on mature IC products.

[edit] Science and engineering

In recent years, data mining has been widely used in area of science and engineering, such as bioinformatics, genetics, medicine, education, and electrical power engineering.

In the area of study on human genetics, the important goal is to understand the mapping relationship between the inter-individual variation in human DNA sequences and variability in disease susceptibility. In lay terms, it is to find out how the changes in an individual's DNA sequence affect the risk of developing common diseases such as cancer. This is very important to help improve the diagnosis, prevention and treatment of the diseases. The data mining technique that is used to perform this task is known as multifactor dimensionality reduction.[11]

In the area of electrical power engineering, data mining techniques have been widely used for condition monitoring of high voltage electrical equipment. The purpose of condition monitoring is to obtain valuable information on the insulation's health status of the equipment. Data clustering such as self-organizing map (SOM) has been applied on the vibration monitoring and analysis of transformer on-load tap-changers(OLTCS). Using vibration monitoring, it can be observed that each tap change operation generates a signal that contains information about the condition of the tap changer contacts and the drive mechanisms. Obviously, different tap positions will generate different signals. However, there was considerable variability amongst normal condition signals for the exact same tap position. SOM has been applied to detect abnormal conditions and to estimate the nature of the abnormalities.[12]

Data mining techniques have also been applied for dissolved gas analysis (DGA) on power transformers. DGA, as a diagnostics for power transformer, has been available for centuries. Data mining techniques such as SOM has been applied to analyse data and to determine trends which are not obvious to the standard DGA ratio techniques such as Duval Triangle.[13]

A fourth area of application for data mining in science/engineering is within educational research, where data mining has been used to study the factors leading students to choose to engage in behaviors which reduce their learning[14] and to understand the factors influencing university student retention.[15]

Other examples of applying data mining technique applications are biomedical data facilitated by domain ontologies,[16] mining clinical trial data,[17] traffic analysis using SOM,[18] et cetera.

[edit] A typical example

A useful example is the large database of thousands of beautiful photos of all kind, collected - and enhanced almost daily - since more than a decade by the Norwegian "anonymous" Svein Ulvund. This huge database is available under www.vossnow.net, [1] (Voss is a town in the Norwegian district of Hordaland near Bergen).

To exploit that photo-collection is not easy. For example, as a typical task, one may want to find out photos from a specific place, e.g. the famous Nærøyfjord nearby. Note that the name of that fjord involves letters which are not easily available on a non-Norwegian keyboard. However, to get around that problem one can find out towns on the fjord without the above-mentioned unfortunate property, e.g. Gudvangen. This can be done with the help of a conventional map, or on the internet. Thus, finally one enters the searchpage of the database, using the two entries Gudv (which suffices for Gudvangen) and the year and month, e.g. Mai 2006, say (not May 2006, since the search machine "speaks Norsk"). Then one gets still a rather large collection of photos from the Nærøyfjord, but now the number is reduced in such a way that it makes sense to have a final selection, either personally or otherwise.

This simple example also makes two points obvious: (i) the necessity to automatize the process and (ii) some of the problems involved.

[edit] See also

[edit] References

  1. ^ W. Frawley and G. Piatetsky-Shapiro and C. Matheus (Fall 1992). "Knowledge Discovery in Databases: An Overview". AI Magazine: pp. 213-228. ISSN 0738-4602. 
  2. ^ D. Hand, H. Mannila, P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge, MA. ISBN 0-262-08290-X. 
  3. ^ Ellen Monk, Bret Wagner (2006). Concepts in Enterprise Resource Planning, Second Edition. Thomson Course Technology, Boston, MA. ISBN 0-619-21663-8. 
  4. ^ Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons. ISBN 0471228524. 
  5. ^ Chip Pitts (March 15, 2007). "The End of Illegal Domestic Spying? Don't Count on It". Wash. Spec.. .
  6. ^ K.A. Taipale (December 15, 2003). "Data Mining and Domestic Security: Connecting the Dots to Make Sense of Data". Colum. Sci. & Tech. L. Rev. 5 (2). SSRN 546782 / OCLC 45263753. .
  7. ^ John Resig, Ankur Teredesai (2004). "A Framework for Mining Instant Messaging Services". In Proceedings of the 2004 SIAM DM Conference. .
  8. ^ Stephen Haag et al.. Management Information Systems for the information age, pp 28. ISBN 0-07-095569-7. 
  9. ^ Ellen Monk, Bret Wagner (2006). Concepts in Enterprise Resource Planning, Second Edition. Thomson Course Technology, Boston, MA. ISBN 0-619-21663-8. 
  10. ^ http://web.engr.oregonstate.edu/~tgd/publications/kdd2000-dlft.pdf
  11. ^ Xingquan Zhu, Ian Davidson (2007). Knowledge Discovery and Data Mining: Challenges and Realities. Hershey, New Your, pp 18. ISBN 978-159904252-7. 
  12. ^ A.J. McGrail, E.Gulski, and al.. "Data Mining Techniques to Asses the Condition of High Voltage Electrical Plant". .
  13. ^ A.J. McGrail, E.Gulski, and al.. "Data Mining Techniques to Asses the Condition of High Voltage Electrical Plant". .
  14. ^ R.Baker. "Is Gaming the System State-or-Trait? Educational Data Mining Through the Multi-Contextual Application of a Validated Behavioral Model". 
  15. ^ J.F. Superby, J-P. Vandamme, N. Meskens. "Determination of factors influencing the achievement of the first-year university students using data mining methods". 
  16. ^ Xingquan Zhu, Ian Davidson (2007). Knowledge Discovery and Data Mining: Challenges and Realities. Hershey, New Your, pp 163-189. ISBN 978-159904252-7. 
  17. ^ Xingquan Zhu, Ian Davidson (2007). Knowledge Discovery and Data Mining: Challenges and Realities. Hershey, New Your, pp 31-48. ISBN 978-159904252-7. 
  18. ^ Yudong Chen, Yi Zhang, Jianming Hu, Xiang Li. "Traffic Data Analysis Using Kernel PCA and Self-Organizing Map". .

[edit] Sources

  • Kurt Thearling, An Introduction to Data Mining (also available is a corresponding online tutorial)
  • Dean Abbott, I. Philip Matkovsky, and John Elder IV, Ph.D. An Evaluation of High-end Data Mining Tools for Fraud Detection published a comparative analysis of major high-end data mining software tools that was presented at the 1998 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, October 12-14, 1998.
  • Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.
  • Peng, Y., Kou, G., Shi, Y. and Chen, Z. "A Systemic Framework for the Field of Data Mining and Knowledge Discovery", in Proceeding of workshops on The Sixth IEEE International Conference on Data Mining Technique (ICDM), 2006

[edit] Books

[edit] External links