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Mining or Data Search – What's the difference and why make a difference for the future?
Exploring data or research data – What is the difference and why the difference in the future?
Data search and data mining?
Nowadays, all do our data search on the Internet today with an Internet search engine such as www.google.com or www.yahoo.com or tastes. Everyone knows it is not unusual to get a blow by the millions of search. In the best case we only a couple of 10 thousand hits on our keyword we searched. How is it possible for us to extract all knowledge of all the successes that we find our research? And how can we qualify the hits we get from these, too? This is where we can differentiate between data retrieval and data mining. Data exploration goes beyond research as it tries to find relationships and make investigations or unstructured data and transform non-traditional knowledge rather than other data structure.
Data Mining
Mining Data is a broad area of interest to many different groups of people and organizations. There are a lot of literature, conferences, programs available to help us in our exploration data, although this there are many outstanding problems of applied research in computer science in areas such as artificial intelligence (AI) Computer Architecture and Engineering (ARC). Database management systems (DBMS), graphics and human-computer interaction (GHCI), the operating systems and networks (OSNT) system programming (SP), Scientific Computing (SCI), Security (SEC) and, finally, the theory (TU).
Data Mining (DM) and also called Knowledge Discovery Database (KDD) has grown to the point that it became our databases larger and more complex. Data is increasingly the text as it now reads. All data can be searched in the traditional manner, such as photos. Rarely find what you need headers, keywords in content the Web and its analogs. You have to dig deeper to get what you really look. Therefore, the database knowledge discovery or data mining consists of several components. It includes the discovery data cleaning and preparation and visualization is a key element (and can be very problematic). It often involves a correlation model search, etc, and automated and objective classification.
This includes data modeling and testing of models that still relies heavily data type, field of study (science, commerce, etc), the nature of the problem and so on. In general, data mining algorithms are incarnations of estimates of statistics
Industry
He has developed an industry expert marketing keywords Years later, say these experts to give you the best success rate on your website using the right keywords in your website. This may be true, But is this all you need to be successful on the web? Or does it apart from others as individuals or as a society? I'm not so sure, and I think it's really a marketing exaggeration, however, should not be dismissed, but the approach must come from another place for you to stand out in the home or business market.
Apart from the Internet industry and strong interest on the keywords we need to address the issue of data recovery and advanced research, as well as use of Internet search engines. We must first see what type of research performed in this field.
Search Data Mining – History
Do we have the ability to perform actual data mining, and more importantly, we have spaces in this type of research activity – the exploration of the data? We also need to have a understanding of who the main contributors to the information on the Internet and how these objectives and actual information. This will help transform information into knowledge.
Search Data Mining - Secondary Commercial
Yes we have several private companies such as woodland, which investment income from commercial text mining, and we Entopia, Inc. doing social network analysis and mining and Inxight is a leading provider of software solutions business for information discovery data unstructured. Array Biopharma Then, using visualization and data mining to decipher the chemical and biological.
Search Data Mining – academic
Several universities around the world dealing with data mining as an issue on its agenda. Yet few are really delve into this subject, and therefore much of the research that we see today, is founded by private commercial interests.
However, there is an academic environment that we make use of data mining as one of its branches of computer science, and California is in the U.S. Tech. Professor SG Djorgovski heads of initiation and development in this area and clearly shows the need for education and research on this topic.
Abstract
Do we understand the ramifications of our database and how we can extract from them, my answer is always no!
We do not understand that people or businesses in value of all available data and the little knowledge that we extracted from them in our daily use. I walked in the social networking sites on the Internet and use of extraction knowledge through his few tools Social Network Analysis, and that parts of the industry as pharmaceuticals, patents of companies using the discovery of knowledge databases or data mining in their daily work. But given the fact that the Internet is still very fragile and users are still Immature, struggle with the fact that we do not the power we have on hand. As individuals to give our intelligence or as some in the industry, intelligence amplifiers or organizations or companies, to create a commercial advantage or to create new ideas or to become innovators in the field of real interest. Also as a marketing tool Data miners are required to be real winners.
Afterwords
I few key people resource reference for you to explore thoroughly, and there. That is, if you want to see more on this subject. Mind you, this is not a comprehensive reference library, but only for the purpose of putting in the subject and understand how data mining is and how many angles to this issue.
Gregory Piatetsky Shapiro-KDNuggets
"The Andy Pryke my data"
ACM Group Special Interest special knowledge discovery in databases and newsletter, "Explorations"
Andrew Moore and mining statistics tutorials of data
The Classification Society of North America (CSNA)
Weka package
David Dowe mixture models
Fionn Murtagh multivariate analysis software data
CMU Statlib
StatCodes to PSU
About the Author
Stig-Arne Kristoffersen
An Explorer
www.lulu.com/stig
Facebook (UCLA 2009)
