Data Mining - Revision history

User at 15:28, 15 March 2023

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User at 22:42, 29 November 2022

2022-11-29T22:42:52Z

User: The LinkTitles extension automatically added links to existing pages (https://github.com/bovender/LinkTitles).

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User: /* See Also */

2018-12-19T23:45:14Z

User: Data mining is a process used by companies to turn raw data into useful information. Data mining depends on effective data collection, warehousing and computer processing.

2018-12-19T23:44:11Z

Data mining is a process used by companies to turn raw data into useful information. Data mining depends on effective data collection, warehousing and computer processing.

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User: Created page with "'''Content Coming Soon'''"

2018-12-05T19:11:38Z

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'''Content Coming Soon'''

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 *[https://www.emeraldinsight.com/doi/abs/10.1108/14684520910985800 Social Implications of Data Mining and Information Privacy: Interdisciplinary Frameworks and Solutions]
 *[http://content.time.com/time/magazine/article/0,9171,2058205,00.html Data Mining: How Companies Now Know Everything About You]
-*https://www.ngdata.com/what-is-data-mining/ What Is Data Mining? ]
+*[https://www.ngdata.com/what-is-data-mining/ What Is Data Mining?]
 *[http://www.dsi.unive.it/~marek/files/06%20-%20datamining Data Mining Concepts and Techniques]
 *[http://www.exinfm.com/pdffiles/intro_dm.pdf Introduction to Data Mining]
 *[https://pdfs.semanticscholar.org/f44d/2c02e22ae27364e0bcfbfcb5bed74b0aa2e1.pdf A Brief Overview on Data Mining Survey]
 __NOTOC__

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-Trends in Data Mining<ref>[https://www.flatworldsolutions.com/data-management/articles/data-mining-future-trends.php 5 Important Future Trends in Data Mining]</ref> ==
+== Trends in Data Mining<ref>[https://www.flatworldsolutions.com/data-management/articles/data-mining-future-trends.php 5 Important Future Trends in Data Mining]</ref> ==
 Businesses that have been slow in adopting the process of data mining are now catching up with the others. Extracting important information through the process of data mining is widely used to make critical business decisions. In the coming decade, we can expect data mining to become as ubiquitous as some of the more prevalent technologies used today. Some of the key data mining trends for the future include -
 *Multimedia Data Mining: This is one of the latest methods which is catching up because of the growing ability to capture useful data accurately. It involves the extraction of data from different kinds of multimedia sources such as audio, text, hypertext, video, images, etc., and the data is converted into a numerical representation in different formats. This method can be used in clustering and classifications, performing similarity checks, and also to identify associations.

← Older revision		Revision as of 22:42, 29 November 2022
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	'''Historical Development of Data Mining'''<ref>Data Mining - Etymology and Background [https://en.wikipedia.org/wiki/Data_mining Wikipedia]</ref><br />		'''Historical Development of Data Mining'''<ref>Data Mining - Etymology and Background [https://en.wikipedia.org/wiki/Data_mining Wikipedia]</ref><br />
−	In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies 1983. Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" (positive) to "fishing" or "snooping" (negative). The term data mining appeared around 1990 in the database community, generally with positive connotations. For a short time in 1980s, a phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Workstation; researchers consequently turned to data mining. Other terms used include data archaeology, information harvesting, information discovery, knowledge extraction, etc. Gregory Piatetsky-Shapiro coined the term "knowledge discovery in databases" for the first workshop on the same topic (KDD-1989) and this term became more popular in AI and machine learning community. However, the term data mining became more popular in the business and press communities. Currently, the terms data mining and knowledge discovery are used interchangeably. In the academic community, the major forums for research started in 1995 when the First International Conference on Data Mining and Knowledge Discovery (KDD-95) was started in Montreal under AAAI sponsorship. It was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, in 1996, Usama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding editor-in-chief. Later he started the SIGKDDD Newsletter SIGKDD Explorations. The KDD International conference became the primary highest [[quality]] conference in data mining with an acceptance rate of research paper submissions below 18%. The journal Data Mining and Knowledge Discovery is the primary research journal of the field. The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing [[power]] of computer technology has dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns in large data sets. It bridges the gap from applied [[statistics]] and artificial intelligence (which usually provide the mathematical background) to database [[management]] by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever larger data sets.	+	In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies 1983. Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" (positive) to "fishing" or "snooping" (negative). The term data mining appeared around 1990 in the database community, generally with positive connotations. For a short time in 1980s, a phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Workstation; researchers consequently turned to data mining. Other terms used include data archaeology, information harvesting, information discovery, knowledge extraction, etc. Gregory Piatetsky-Shapiro coined the term "knowledge discovery in databases" for the first workshop on the same topic (KDD-1989) and this term became more popular in AI and machine learning community. However, the term data mining became more popular in the business and press communities. Currently, the terms data mining and knowledge discovery are used interchangeably. In the academic community, the major forums for research started in 1995 when the First International Conference on Data Mining and Knowledge Discovery (KDD-95) was started in Montreal under AAAI sponsorship. It was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, in 1996, Usama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding editor-in-chief. Later he started the SIGKDDD Newsletter SIGKDD Explorations. The KDD International conference became the primary highest [[quality]] conference in data mining with an acceptance rate of research paper submissions below 18%. The journal Data Mining and Knowledge Discovery is the primary research journal of the field. The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database [[management]] by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever larger data sets.

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 [[Data_Analytics|Data Analytics]]<br />
 [[Predictive_Analytics|Predictive Analytics]]
 ===References===