Examples of data mining

Data mining, the process of discovering patterns in large data sets, has been used in many applications.

## Contents

* 1 Business
* 2 Science and engineering
* 3 Human rights
* 4 Medical data mining
* 5 Spatial data mining
* 6 Temporal data mining
* 7 Sensor data mining
* 8 Visual data mining
* 9 Music data mining
* 10 Surveillance
* 11 Pattern mining
* 12 Subject-based data mining
* 13 Knowledge grid
* 14 References
* 15 External links

## Business

In business, data mining is the analysis of historical business activities, stored as static data in data warehouse databases. The goal is to reveal hidden patterns and trends. Data mining software uses advanced pattern recognition algorithms to sift through large amounts of data to assist in discovering previously unknown strategic business information. Examples of what businesses use data mining for is to include performing market analysis to identify new product bundles, finding the root cause of manufacturing problems, to prevent customer attrition and acquire new customers, cross-selling to existing customers, and profiling customers with more accuracy.[1]

* In today's world raw data is being collected by companies at an exploding rate. For example, Walmart processes over 20 million point-of-sale transactions every day. This information is stored in a centralized database, but would be useless without some type of data mining software to analyze it. If Walmart analyzed their point-of-sale data with data mining techniques they would be able to determine sales trends, develop marketing campaigns, and more accurately predict customer loyalty.[2][3]
* Categorization of the items available in the e-commerce site is a fundamental problem. A correct item categorization system is essential for user experience as it helps determine the items relevant to him for search and browsing. Item categorization can be formulated as a supervised classification problem in data mining where the categories are the target classes and the features are the words composing some textual description of the items. One of the approaches is to find groups initially which are similar and place them together in a latent group. Now given a new item, first classify into a latent group which is called coarse level classification. Then, do a second round of classification to find the category to which the item belongs to.[4]
* Every time a credit card or a store loyalty card is being used, or a warranty card is being filled, data is being collected about the user's behavior. Many people find the amount of information stored about us from companies, such as Google, Facebook, and Amazon, disturbing and are concerned about privacy. Although there is the potential for our personal data to be used in harmful, or unwanted, ways it is also being used to make our lives better. For example, Ford and Audi hope to one day collect information about customer driving patterns so they can recommend safer routes and warn drivers about dangerous road conditions.[5]
* Data mining in customer relationship management applications can contribute significantly to the bottom line.[citation needed] Rather than randomly contacting a prospect or customer through a call center or sending mail, a company can concentrate its efforts on prospects that are predicted to have a high likelihood of responding to an offer. More sophisticated methods may be used to optimize resources across campaigns so that one may predict to which channel and to which offer an individual is most likely to respond (across all potential offers). Additionally, sophisticated applications could be used to automate mailing. Once the results from data mining (potential prospect/customer and channel/offer) are determined, this "sophisticated application" can either automatically send an e-mail or a regular mail. Finally, in cases where many people will take an action without an offer, "uplift modeling" can be used to determine which people have the greatest increase in response if given an offer. Uplift modeling thereby enables marketers to focus mailings and offers on persuadable people, and not to send offers to people who will buy the product without an offer. Data clustering can also be used to automatically discover the segments or groups within a customer data set.
* Businesses employing data mining may see a return on investment, but also they recognize that the number of predictive models can quickly become very large. For example, rather than using one model to predict how many customers will churn, a business may choose to build a separate model for each region and customer type. In situations where a large number of models need to be maintained, some businesses turn to more automated data mining methodologies.
* Data mining can be helpful to human resources (HR) 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.[6]
* Market basket analysis has been used to identify the purchase patterns of the Alpha Consumer. Analyzing the data collected on this type of user has allowed companies to predict future buying trends and forecast supply demands.[citation needed]
* Data mining is a highly effective tool in the catalog marketing industry.[citation needed] Catalogers have a rich database of history of their customer transactions for millions of customers dating back a number of years. Data mining tools can identify patterns among customers and help identify the most likely customers to respond to upcoming mailing campaigns.
* Data mining for business applications can be integrated into a complex modeling and decision making process.[7] LIONsolver uses Reactive business intelligence (RBI) to advocate a "holistic" approach that integrates data mining, modeling, and interactive visualization into an end-to-end discovery and continuous innovation process powered by human and automated learning.[8]
* In the area of decision making, the RBI approach has been used to mine knowledge that is progressively acquired from the decision maker, and then self-tune the decision method accordingly.[9] The relation between the quality of a data mining system and the amount of investment that the decision maker is willing to make was formalized by providing an economic perspective on the value of “extracted knowledge” in terms of its payoff to the organization[7] This decision-theoretic classification framework[7] was applied to a real-world semiconductor wafer manufacturing line, where decision rules for effectively monitoring and controlling the semiconductor wafer fabrication line were developed.[10]
* An example of data mining related to an integrated-circuit (IC) production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing."[11] In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a system of mining historical die-test data to create a probabilistic model of patterns of die failure. These patterns 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. Other examples[12][13] of the application of data mining methodologies in semiconductor manufacturing environments suggest that data mining methodologies may be particularly useful when data is scarce, and the various physical and chemical parameters that affect the process exhibit highly complex interactions. Another implication is that on-line monitoring of the semiconductor manufacturing process using data mining may be highly effective.

## Science and engineering

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

* In the study of human genetics, sequence mining helps address the important goal of understanding the mapping relationship between the inter-individual variations in human DNA sequence and the variability in disease susceptibility. In simple terms, it aims to find out how the changes in an individual's DNA sequence affects the risks of developing common diseases such as cancer, which is of great importance to improving methods of diagnosing, preventing, and treating these diseases. One data mining method that is used to perform this task is known as multifactor dimensionality reduction.[14]
* In the area of electrical power engineering, data mining methods have been widely used for condition monitoring of high voltage electrical equipment. The purpose of condition monitoring is to obtain valuable information on, for example, the status of the insulation (or other important safety-related parameters). Data clustering techniques – such as the self-organizing map (SOM), have been applied to 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 exactly the same tap position. SOM has been applied to detect abnormal conditions and to hypothesize about the nature of the abnormalities.[15]
* Data mining methods have been applied to dissolved gas analysis (DGA) in power transformers. DGA, as a diagnostics for power transformers, has been available for many years. Methods such as SOM has been applied to analyze generated data and to determine trends which are not obvious to the standard DGA ratio methods (such as Duval Triangle).[15]
* In educational research, where data mining has been used to study the factors leading students to choose to engage in behaviors which reduce their learning,[16] and to understand factors influencing university student retention.[17] A similar example of social application of data mining is its use in expertise finding systems, whereby descriptors of human expertise are extracted, normalized, and classified so as to facilitate the finding of experts, particularly in scientific and technical fields. In this way, data mining can facilitate institutional memory.
* Data mining methods of biomedical data facilitated by domain ontologies,[18] mining clinical trial data,[19] and traffic analysis using SOM.[20]
* In adverse drug reaction surveillance, the Uppsala Monitoring Centre has, since 1998, used data mining methods to routinely screen for reporting patterns indicative of emerging drug safety issues in the WHO global database of 4.6 million suspected adverse drug reaction incidents.[21] Recently, similar methodology has been developed to mine large collections of electronic health records for temporal patterns associating drug prescriptions to medical diagnoses.[22]
* Data mining has been applied to software artifacts within the realm of software engineering: Mining Software Repositories.
* In the field of microbiology, data mining methods have been used for predicting population behavior of bacteria in food.[23]

## Human rights

Data mining of government records – particularly records of the justice system (i.e., courts, prisons) – enables the discovery of systemic human rights violations in connection to generation and publication of invalid or fraudulent legal records by various government agencies.[24][25]

## Medical data mining

Some machine learning algorithms can be applied in medical field as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in databases. One of these classifiers (called Prototype exemplar learning classifier (PEL-C)[26] is able to discover syndromes as well as atypical clinical cases.

A current medical field that utilizes the process of data mining is Metabolomics, which is the investigation and study of biological molecules and how their interaction with bodily fluids, cells, tissues, etc. is characterized.[27] Metabolomics is a very data heavy subject, and often involves sifting through massive amounts of irrelevant data before finding any conclusions. Data mining has allowed this relatively new field of medical research to grow considerably within the last decade, and will likely be the method of which new research is found within the subject.[27]

In 2011, the case of Sorrell v. IMS Health, Inc., decided by the Supreme Court of the United States, ruled that pharmacies may share information with outside companies. This practice was authorized under the 1st Amendment of the Constitution, protecting the "freedom of speech."[28] However, the passage of the Health Information Technology for Economic and Clinical Health Act (HITECH Act) helped to initiate the adoption of the electronic health record (EHR) and supporting technology in the United States.[29] The HITECH Act was signed into law on February 17, 2009 as part of the American Recovery and Reinvestment Act (ARRA) and helped to open the door to medical data mining.[30] Prior to the signing of this law, estimates of only 20% of United States-based physicians were utilizing electronic patient records.[29] Søren Brunak notes that “the patient record becomes as information-rich as possible” and thereby “maximizes the data mining opportunities.”[29] Hence, electronic patient records further expands the possibilities regarding medical data mining thereby opening the door to a vast source of medical data analysis.

## Spatial data mining

Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis. Particularly, most contemporary GIS have only very basic spatial analysis functionality. The immense explosion in geographically referenced data occasioned by developments in IT, digital mapping, remote sensing, and the global diffusion of GIS emphasizes the importance of developing data-driven inductive approaches to geographical analysis and modeling.

Data mining offers great potential benefits for GIS-based applied decision-making. Recently, the task of integrating these two technologies has become of critical importance, especially as various public and private sector organizations possessing huge databases with thematic and geographically referenced data begin to realize the huge potential of the information contained therein. Among those organizations are:

* Offices requiring analysis or dissemination of geo-referenced statistical data
* Public health services searching for explanations of disease clustering
* Environmental agencies assessing the impact of changing land-use patterns on climate change
* Geo-marketing companies doing customer segmentation based on spatial location.

Challenges in Spatial mining: Geospatial data repositories tend to be very large. Moreover, existing GIS datasets are often splintered into feature and attribute components that are conventionally archived in hybrid data management systems. Algorithmic requirements differ substantially for relational (attribute) data management and for topological (feature) data management.[31] Related to this is the range and diversity of geographic data formats, which present unique challenges. The digital geographic data revolution is creating new types of data formats beyond the traditional "vector" and "raster" formats. Geographic data repositories increasingly include ill-structured data, such as imagery and geo-referenced multi-media.[32]

There are several critical research challenges in geographic knowledge discovery and data mining. Miller and Han[33] offer the following list of emerging research topics in the field:

* Developing and supporting geographic data warehouses (GDW's): Spatial properties are often reduced to simple aspatial attributes in mainstream data warehouses. Creating an integrated GDW requires solving issues of spatial and temporal data interoperability – including differences in semantics, referencing systems, geometry, accuracy, and position.
* Better spatio-temporal representations in geographic knowledge discovery: Current geographic knowledge discovery (GKD) methods generally use very simple representations of geographic objects and spatial relationships. Geographic data mining methods should recognize more complex geographic objects (i.e., lines and polygons) and relationships (i.e., non-Euclidean distances, direction, connectivity, and interaction through attributed geographic space such as terrain). Furthermore, the time dimension needs to be more fully integrated into these geographic representations and relationships.
* Geographic knowledge discovery using diverse data types: GKD methods should be developed that can handle diverse data types beyond the traditional raster and vector models, including imagery and geo-referenced multimedia, as well as dynamic data types (video streams, animation).

## Temporal data mining

Data may contain attributes generated and recorded at different times. In this case finding meaningful relationships in the data may require considering the temporal order of the attributes. A temporal relationship may indicate a causal relationship, or simply an association.[citation needed]

## Sensor data mining

Wireless sensor networks can be used for facilitating the collection of data for spatial data mining for a variety of applications such as air pollution monitoring.[34] A characteristic of such networks is that nearby sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the techniques for in-network data aggregation and mining. By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.[35]

## Visual data mining

In the process of turning from analog into digital, large data sets have been generated, collected, and stored discovering statistical patterns, trends and information which is hidden in data, in order to build predictive patterns. Studies suggest visual data mining is faster and much more intuitive than is traditional data mining.[36][37][38] See also Computer vision.

## Music data mining

Data mining techniques, and in particular co-occurrence analysis, has been used to discover relevant similarities among music corpora (radio lists, CD databases) for purposes including classifying music into genres in a more objective manner.[39]

## Surveillance

Data mining has been used by the U.S. government. Programs include the Total Information Awareness (TIA) program, Secure Flight (formerly known as Computer-Assisted Passenger Prescreening System (CAPPS II)), Analysis, Dissemination, Visualization, Insight, Semantic Enhancement (ADVISE),[40] and the Multi-state Anti-Terrorism Information Exchange (MATRIX).[41] These programs have been discontinued due to controversy over whether they violate the 4th Amendment to the United States Constitution, although many programs that were formed under them continue to be funded by different organizations or under different names.[42]

In the context of combating terrorism, two particularly plausible methods of data mining are "pattern mining" and "subject-based data mining".

## Pattern mining

"Pattern mining" is a data mining method that involves finding existing patterns in data. In this context patterns often means association rules. The original motivation for searching association rules came from the desire to analyze supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. For example, an association rule "beer ⇒ potato chips (80%)" states that four out of five customers that bought beer also bought potato chips.

In the context of pattern mining as a tool to identify terrorist activity, the National Research Council provides the following definition: "Pattern-based data mining looks for patterns (including anomalous data patterns) that might be associated with terrorist activity — these patterns might be regarded as small signals in a large ocean of noise."[43][44][45] Pattern Mining includes new areas such a Music Information Retrieval (MIR) where patterns seen both in the temporal and non temporal domains are imported to classical knowledge discovery search methods.

## Subject-based data mining

"Subject-based data mining" is a data mining method involving the search for associations between individuals in data. In the context of combating terrorism, the National Research Council provides the following definition: "Subject-based data mining uses an initiating individual or other datum that is considered, based on other information, to be of high interest, and the goal is to determine what other persons or financial transactions or movements, etc., are related to that initiating datum."[44]

## Knowledge grid

Knowledge discovery "On the Grid" generally refers to conducting knowledge discovery in an open environment using grid computing concepts, allowing users to integrate data from various online data sources, as well make use of remote resources, for executing their data mining tasks. The earliest example was the Discovery Net,[46][47] developed at Imperial College London, which won the "Most Innovative Data-Intensive Application Award" at the ACM SC02 (Supercomputing 2002) conference and exhibition, based on a demonstration of a fully interactive distributed knowledge discovery application for a bioinformatics application. Other examples include work conducted by researchers at the University of Calabria, who developed a Knowledge Grid architecture for distributed knowledge discovery, based on grid computing.[48][49]

## References

1. ↑ O'Brien, J. A., & Marakas, G. M. (2011). Management Information Systems. New York, NY: McGraw-Hill/Irwin.
2. ↑ Alexander, D. (n.d.). Data Mining. Retrieved from The University of Texas at Austin: College of Liberal Arts: http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/
3. ↑ "Daniele Medri: Big Data & Business: An on-going revolution". Statistics Views. 21 Oct 2013. http://www.statisticsviews.com/details/feature/5393251/Big-Data--Business-An-on-going-revolution.html.
4. ↑ "Large Scale Item Categorization". http://labs.ebay.com/wp-content/uploads/2012/10/Jean-David-Ruvini-Large-scale_Item.pdf.
5. ↑ Goss, S. (2013, April 10). Data-mining and our personal privacy. Retrieved from The Telegraph: "Data-mining and our personal privacy | the Sun News | Macon.com". http://www.macon.com/2013/04/10/2429775/data-mining-and-our-personal-privacy.html.
6. ↑ Monk, Ellen; Wagner, Bret (2006). Concepts in Enterprise Resource Planning, Second Edition. Boston, MA: Thomson Course Technology. ISBN 978-0-619-21663-4. OCLC 224465825.
7. ↑ 7.0 7.1 7.2 Elovici, Yuval; Braha, Dan (2003). "A Decision-Theoretic Approach to Data Mining". IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 33 (1): 42–51. doi:10.1109/TSMCA.2003.812596. http://necsi.edu/affiliates/braha/IEEE_Decision_Theoretic.pdf.
8. ↑ Battiti, Roberto; and Brunato, Mauro; Reactive Business Intelligence. From Data to Models to Insight, Reactive Search Srl, Italy, February 2011. ISBN:978-88-905795-0-9.
9. ↑ Battiti, Roberto; Passerini, Andrea (2010). "Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker". IEEE Transactions on Evolutionary Computation 14 (15): 671–687. doi:10.1109/TEVC.2010.2058118. http://rtm.science.unitn.it/~battiti/archive/bcemo.pdf.
10. ↑ Braha, Dan; Elovici, Yuval; Last, Mark (2007). "Theory of actionable data mining with application to semiconductor manufacturing control". International Journal of Production Research 45 (13): 3059–3084. doi:10.1080/00207540600654475. http://necsi.edu/affiliates/braha/TPRS_A_165421_O.pdf.
11. ↑ Fountain, Tony; Dietterich, Thomas; and Sudyka, Bill (2000); Mining IC Test Data to Optimize VLSI Testing, in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM Press, pp. 18–25
12. ↑ Braha, Dan; Shmilovici, Armin (2002). "Data Mining for Improving a Cleaning Process in the Semiconductor Industry". IEEE Transactions on Semiconductor Manufacturing 15 (1): 91–101. doi:10.1109/66.983448. http://necsi.edu/affiliates/braha/IEEE-Cleaning_02.pdf.
13. ↑ Braha, Dan; Shmilovici, Armin (2003). "On the Use of Decision Tree Induction for Discovery of Interactions in a Photolithographic Process". IEEE Transactions on Semiconductor Manufacturing 16 (4): 644–652. doi:10.1109/TSM.2003.818959. http://necsi.edu/affiliates/braha/IEEE_Decision_Trees.pdf.
14. ↑ Zhu, Xingquan; Davidson, Ian (2007). Knowledge Discovery and Data Mining: Challenges and Realities. New York, NY: Hershey. p. 18. ISBN 978-1-59904-252-7.
15. ↑ 15.0 15.1 McGrail, Anthony J.; Gulski, Edward; Allan, David; Birtwhistle, David; Blackburn, Trevor R.; Groot, Edwin R. S.. "Data Mining Techniques to Assess the Condition of High Voltage Electrical Plant". CIGRÉ WG 15.11 of Study Committee 15.
16. ↑ Baker, Ryan S. J. d.. "Is Gaming the System State-or-Trait? Educational Data Mining Through the Multi-Contextual Application of a Validated Behavioral Model". Workshop on Data Mining for User Modeling 2007.
17. ↑ Superby Aguirre, Juan Francisco; Vandamme, Jean-Philippe; Meskens, Nadine. "Determination of factors influencing the achievement of the first-year university students using data mining methods". Workshop on Educational Data Mining 2006.
18. ↑ Zhu, Xingquan; Davidson, Ian (2007). Knowledge Discovery and Data Mining: Challenges and Realities. New York, NY: Hershey. pp. 163–189. ISBN 978-1-59904-252-7.
19. ↑ Zhu, Xingquan; Davidson, Ian (2007). Knowledge Discovery and Data Mining: Challenges and Realities. New York, NY: Hershey. pp. 31–48. ISBN 978-1-59904-252-7.
20. ↑ Chen, Yudong; Zhang, Yi; Hu, Jianming; Li, Xiang (2006). "Traffic Data Analysis Using Kernel PCA and Self-Organizing Map". 2006 IEEE Intelligent Vehicles Symposium. pp. 472–477. doi:10.1109/IVS.2006.1689673. ISBN 978-4-901122-86-3.
21. ↑ Bate, Andrew; Lindquist, Marie; Edwards, I. Ralph; Olsson, Sten; Orre, Roland; Lansner, Anders; de Freitas, Rogelio Melhado (Jun 1998). "A Bayesian neural network method for adverse drug reaction signal generation". European Journal of Clinical Pharmacology 54 (4): 315–21. doi:10.1007/s002280050466. PMID 9696956. http://dml.cs.byu.edu/~cgc/docs/atdm/W11/BCPNN-ADR.pdf. [yes|permanent dead link|dead link}}]
22. ↑ Norén, G. Niklas; Bate, Andrew; Hopstadius, Johan; Star, Kristina; and Edwards, I. Ralph (2008); Temporal Pattern Discovery for Trends and Transient Effects: Its Application to Patient Records. Proceedings of the Fourteenth International Conference on Knowledge Discovery and Data Mining (SIGKDD 2008), Las Vegas, NV, pp. 963–971.
23. ↑ Hiura, Satoko; Koseki, Shige; Koyama, Kento (2021-05-19). "Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database" (in en). Scientific Reports 11 (1): 10613. doi:10.1038/s41598-021-90164-z. ISSN 2045-2322. PMID 34012066. Bibcode: 2021NatSR..1110613H.
24. ↑ Zernik, Joseph; Data Mining as a Civic Duty – Online Public Prisoners' Registration Systems, International Journal on Social Media: Monitoring, Measurement, Mining, 1: 84–96 (2010)
25. ↑ Zernik, Joseph; Data Mining of Online Judicial Records of the Networked US Federal Courts, International Journal on Social Media: Monitoring, Measurement, Mining, 1:69–83 (2010)
26. ↑ Gagliardi, F (2011). "Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction". Artificial Intelligence in Medicine 52 (3): 123–139. doi:10.1016/j.artmed.2011.04.002. PMID 21621400.
27. ↑ 27.0 27.1 Martínez-Arranz, Ibon; Mayo, Rebeca; Pérez-Cormenzana, Miriam; Mincholé, Itziar; Salazar, Lorena; Alonso, Cristina; Mato, José M. (2015). "Enhancing metabolomics research through data mining". Journal of Proteomics 127 (Pt B): 275–288. doi:10.1016/j.jprot.2015.01.019. PMID 25668325.
28. ↑ David G. Savage (2011-06-24). "Pharmaceutical industry: Supreme Court sides with pharmaceutical industry in two decisions". Los Angeles Times. http://articles.latimes.com/2011/jun/24/nation/la-na-court-drugs-20110624.
29. ↑ 29.0 29.1 29.2 Goth, Gregory (2012). "Analyzing medical data". Communications of the ACM 55 (6): 13. doi:10.1145/2184319.2184324.
30. ↑ "What is HITECH (Health Information Technology for Economic and Clinical Health) Act of 2009? | Definition from TechTarget". http://searchhealthit.techtarget.com/definition/HITECH-Act.
31. ↑ Healey, Richard G. (1991); Database Management Systems, in Maguire, David J.; Goodchild, Michael F.; and Rhind, David W., (eds.), Geographic Information Systems: Principles and Applications, London, GB: Longman
32. ↑ Camara, Antonio S.; and Raper, Jonathan (eds.) (1999); Spatial Multimedia and Virtual Reality, London, GB: Taylor and Francis
33. ↑ Miller, Harvey J.; and Han, Jiawei (eds.) (2001); Geographic Data Mining and Knowledge Discovery, London, GB: Taylor & Francis
34. ↑ Ma, Y.; Richards, M.; Ghanem, M.; Guo, Y.; Hassard, J. (2008). "Air Pollution Monitoring and Mining Based on Sensor Grid in London". Sensors 8 (6): 3601–3623. doi:10.3390/s8063601. PMID 27879895. Bibcode: 2008Senso...8.3601M.
35. ↑ Ma, Y.; Guo, Y.; Tian, X.; Ghanem, M. (2011). "Distributed Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks". IEEE Sensors Journal 11 (3): 641. doi:10.1109/JSEN.2010.2056916. Bibcode: 2011ISenJ..11..641M.
36. ↑ Zhao, Kaidi; and Liu, Bing; Tirpark, Thomas M.; and Weimin, Xiao; A Visual Data Mining Framework for Convenient Identification of Useful Knowledge
37. ↑ Keim, Daniel A.; Information Visualization and Visual Data Mining
38. ↑ Burch, Michael; Diehl, Stephan; Weißgerber, Peter; Visual Data Mining in Software Archives
39. ↑ Pachet, François; Westermann, Gert; and Laigre, Damien; Musical Data Mining for Electronic Music Distribution , Proceedings of the 1st WedelMusic Conference, Firenze, Italy, 2001, pp. 101–106.
40. ↑ Government Accountability Office, Data Mining: Early Attention to Privacy in Developing a Key DHS Program Could Reduce Risks, GAO-07-293 (February 2007), Washington, DC
41. ↑ Secure Flight Program report, NBC News
42. ↑ "Total/Terrorism Information Awareness (TIA): Is It Truly Dead?". Electronic Frontier Foundation (official website). 2003. http://w2.eff.org/Privacy/TIA/20031003_comments.php.
43. ↑ Agrawal, Rakesh; Mannila, Heikki; Srikant, Ramakrishnan; Toivonen, Hannu; and Verkamo, A. Inkeri; Fast discovery of association rules, in Advances in knowledge discovery and data mining, MIT Press, 1996, pp. 307–328
44. ↑ 44.0 44.1 National Research Council, Protecting Individual Privacy in the Struggle Against Terrorists: A Framework for Program Assessment, Washington, DC: National Academies Press, 2008
45. ↑ Haag, Stephen; Cummings, Maeve; Phillips, Amy (2006). Management Information Systems for the information age. Toronto: McGraw-Hill Ryerson. p. 28. ISBN 978-0-07-095569-1. OCLC 63194770. https://archive.org/details/managementinform0000unse_n7i0/page/28.
46. ↑ Ghanem, Moustafa; Guo, Yike; Rowe, Anthony; Wendel, Patrick (2002). "Grid-based knowledge discovery services for high throughput informatics". Proceedings 11th IEEE International Symposium on High Performance Distributed Computing. pp. 416. doi:10.1109/HPDC.2002.1029946. ISBN 978-0-7695-1686-8.
47. ↑ Ghanem, Moustafa; Curcin, Vasa; Wendel, Patrick; Guo, Yike (2009). "Building and Using Analytical Workflows in Discovery Net". Data Mining Techniques in Grid Computing Environments. pp. 119. doi:10.1002/9780470699904.ch8. ISBN 9780470699904.
48. ↑ Cannataro, Mario; Talia, Domenico (January 2003). "The Knowledge Grid: An Architecture for Distributed Knowledge Discovery". Communications of the ACM 46 (1): 89–93. doi:10.1145/602421.602425. http://grid.deis.unical.it/papers/pdf/CACM2003.pdf. Retrieved 17 October 2011.
49. ↑ Talia, Domenico; Trunfio, Paolo (July 2010). "How distributed data mining tasks can thrive as knowledge services". Communications of the ACM 53 (7): 132–137. doi:10.1145/1785414.1785451. http://grid.deis.unical.it/papers/pdf/CACM2010.pdf. Retrieved 17 October 2011.

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