1. The Core team understand data mining.

2. Data Applied, a web based data mining environment.

3. Apriori algorithm is a classical algorithm in data mining

4. Data mining is used to analyse individuals' buying habits.

5. KNIME: free and commercial machine learning and data mining software.

6. Cross-industry standard process for data mining, known as Crisp-DM, is an open standard process model that describes common approaches used by data mining experts

7. Aidm Information Technology and Services Al Khaldeyah, Makkah 1,395 followers Artificial Intelligence and Data Mining (Aidm) company specializes in developing innovative AI and Data Mining Solutions

8. This topic in development uses data warehouse and data mining technology.

9. The basic step of the data mining has five stages: question definition and subject analysis, data preparation, model build, mode assessment, appraisal and verification of the data mining result.

10. The genetic algorithm plays an important role in area of data mining.

11. This process is called data accumulation or data enhancement in data mining lingo.

12. • Analytical Tools (e.g., Advanced Analysis Tools, Data Mining/Knowledge Discovery, and Knowledge Management);

13. Advanced data-mining techniques and enhanced learning algorithms are also being worked on.

14. For example, data mining techniques might be applied to mine and structure personal information.

15. Himalaya Tools is a suite of programs focusing on new techniques in data mining.

16. Data sources for data mining access are uniformly modelled using the abstract class MiningInputStream.

17. This article introduces the background, basic tasks and methods of Data Mining with pellucid examples.

18. The technology integrates advanced criminal and social network analysis, together with text and data mining methods.

19. The issue of data mining also come into place as countries head towards a cashless society.

20. Using the method of binary system in association rule data mining may produce some redundancy patterns.

21. There are two types of data mining methods integration, they are horizontal integration and vertical integration.

22. Spatial clustering analysis is important method and study content of spatial analysis and spatial data mining.

23. The growth of data mining has led many to worry about invasions of privacy by overzealous marketers.

24. Augustus is designed for statistical and data mining models and produces and consumes models with 10,000s of segments

25. The Commission will continue to support R&D in data-handling technologies, e.g. data mining, analytics or visualisation.

26. The extension method enriches the content of data mining, and provides new tools for building multivalue correlative criteria.

27. Data mining requires data preparation which can uncover information or patterns which may compromise confidentiality and privacy obligations.

28. Mathematical and programming methods of data processing; modelling and prediction; data mining; expert information systems; and decision support systems;

29. In particular, machine learning algorithms together with data mining processes can help in pattern recognition even with unstructured data.

30. University projects from California, Switzerland and Germany try to improve that by methods of formal analysis and data mining.

31. Weimann has argued that data mining could sniff out jihadists or remove information before would-be terrorists see it.

32. Spatial clustering is one of the important research topic in spatial data mining, it is widely applied in spatial analysis.

33. Business intelligence (Bi) is an umbrella term for the technology that enables data preparation, data mining, data management, and data visualization

34. (dental practice management placement) Admi: Air Defense MSE Interface: Admi: Australian DeMining International Pty Ltd: Admi: Agents and Data Mining Interaction (workshop)

35. There are three common personalized recommendatory technologies: information retrieval and extractor, content-based filtering and collaborative filtering, data mining and knowledge discovery.

36. Aiming at the method of binary system conversion in data mining, this paper defines binary system sequences set and its related concepts.

37. In the area of electrical power engineering, data mining methods have been widely used for condition monitoring of high voltage electrical equipment.

38. Depending on all above studies and experiments, we can conclude that using data mining distill prosodic rules in speech synthesis is viable.

39. This add-in can be used alone, but it’s designed to work with Frontline’s Analytic Solver Data Mining add-in, offering powerful forecasting

40. Deep Packet Inspection (and filtering) enables advanced network management, user service, and security functions as well as Internet data mining, eavesdropping, censorship etc.

41. Data mining is an intercrossed subject, involving many fields such as machine learning, model reorganization, induction and deduction, statistics, database and high performance calculation.

42. Such as: the establishment of databank, category analysis and management, data mining and implementation, integration "between proscenium and tiring-room" of Digital Marketing etc.

43. BI is a broad term that encompasses data mining, process analysis, performance benchmarking, and descriptive analytics. BI parses all the data generated by a …

44. This also includes the use of Big Data infrastructures, Data Mining, advanced analytics, predictive analytics and Customer Journey management and xIntelligence: multi-channel analytics.

45. Emerging technology, uses advanced data-mining and software algorithms to detect what the users themselves consider to be "topics", then virtually classifies the documents automatically.

46. Computer software for reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics, all in the field of business intelligence

47. In this paper we give an archetypal design for data mining system, Incremental updating technique is applied in this archetypal system which quickly dealing with large databases.

48. Tolerence operators are kind of operations converting general relation to compatibility relation, It also extends the application range of the compatibility relation-based methods for data mining.

49. In 2015, IBM released a new methodology called Analytics Solutions Unified Method for Data Mining/Predictive Analytics (also known as ASUM-DM) which refines and extends Crisp-DM.

50. Our track in Biostatistics and Data Science is unique as it focuses on data mining and machine learning techniques yet retains the rigor of a traditional Biostatistics program

51. Bi technologies provide historical, current, and predictive views of business operations.Common functions of business intelligence technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining

52. Bursty and Hierarchical Structure in Streams Jon Kleinberg y Abstract A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time

53. What is Anomaly detection? Anomaly detection (aka outlier analysis) is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset’s normal behavior.

54. Design, creation and dissemination of software platforms designed to monitor fraud, merchant and cardholder profiling, data mining and mis-reporting including simple queries regarding retail transactions, compromised account monitoring and alarm

55. It is a practical meaning that forecast intelligently and statistics quickly data in the electric material system to save money and economize labour force by the technology of the Data Mining.

56. This includes new technologies for language, learning, interaction, digital preservation, content access and analytics; intelligent information management systems based on advanced data mining, machine learning, statistical analysis and visual computing technologies.

57. 5 Based on such win-win concept, the concept of Game Theory is adopted to obtain optimal prices of different clusters from the derived demand curves by using data mining techniques.

58. 25 It is a practical meaning that forecast intelligently and statistics quickly data in the electric material system to save money and economize labour force by the technology of the Data Mining.

59. The challenge is to make sure data mining doesn't become data strip mining - that we don't burn down the forest to make a lot of money quick but with no long term value.

60. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering

61. A record of the plays is aggregated and custom reports via data mining can be produced which is specifically targeted to the payment of royalties or license fees to a stakeholder, or to user or industry demographics.

62. Self-service business intelligence (Bi) is an approach to data analytics that enables business users to access and explore data sets even if they don't have a background in Bi or related functions like data mining and statistical analysis

63. Much crucial business data is stored in a range of different data stores, many attached to business applications like ERP.Full-stack Bi tools consolidate all data in a data warehouse which is a relational database designed for data mining instead of transactional

64. Consequently, we devise in this paper a series of level matching algorithms, including Level Matching (Abbreviatedly as LM), Level Matching with Selective Scan (Abbreviatedly as LMS), and Distributed Level Matching (Abbreviatedly as Distibuted LM), to minimize the computing cost needed for the distributed data mining of causality rules

65. Anatomized Learning Problem Given some l-diverse data in the anatomy model, can we learn accurate data mining models? • What is the anatomy model? • What is l-diverse? • Under which assumptions? Anatomy Model Separate data table ( ) into two tables, identifier (𝐼 ) and sensitive table ( ) instead of generalizing records in the same group:

66. Business intelligence (Bi) comBines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions.In practice, you know you’ve got modern business intelligence when you have a comprehensive view of your organization’s data and use that data to drive change, eliminate inefficiencies, and

67. In statistics, data mining and machine learning, bootstrap aggregating The random subspace method, also called attribute Bagging; In mountaineering, peak Bagging In medicine, ventilating a patient with a bag valve mask; In agriculture, the Bagging hook, a form of reap hook or sickle; In drug slang, Bagging is a form of drug abuse akin to huffing

68. Whereas the effectiveness of data-mining is weakened by the needle in the haystack problem of analysts having to filter through the huge quantity of available data; whereas the extent of digital tracks left by law-abiding citizens is even greater than that of criminals and terrorists who make considerable efforts to conceal their identities; and whereas there are significant rates of false positives whereby not only do wholly innocent people come under suspicion resulting in potential invasion of individual privacy but real suspects meanwhile remain unidentified