History of Data Mining (pdf)
Added on 2021-06-14
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P1 Investigate the historical background of data mining.What is Data miningThe process by which the designs of a huge collection of data can be determined and also contains the approaches of database scheme, machine learning and statistics are called data mining. The main work of data mining is to assess the huge amount of data automatically or semi-automatically to extract the unknown, exciting designs like dependencies, statistics and data `1b0020records. Normally it has database techniques like spatial indices. Moreover, these designs may be perceived like a type of brief of data input that can be used for supplementary enquiry. Historic background of Data miningThe concept “Data Mining” was first launched in the 1990s. But, it has a large evaluation history.Fig: Different phases of data miningIn 1763, the paper of Thomas Bayes which was called The Bayes’ Theorem launched for connecting the existing probability.
In 1805, Regression is functioned by Adrien-Marie Legendre and Friedrich Gauss for defining the orbits of figures of the Sun (comets and planets). It is one of the core tools to analyse the probable associations with variables and exact method which is used in the method of least squares.In 1970s, data warehouses agree that the user can transfer from a transaction-oriented technique because of the possibility of keeping the urbane database management system.In 1980s, the phrase Database Mining was trademarked by HNC for the safety purpose of a product which is named Database Mining Workstation. After that, Gregory Piatetsky-Shapirodiscovered the clop Knowledge “Discovery in Databases” (KDD) in 1989. The word Data mining was first introduced in database community in 1990s. Many companies are using data mining for examining and identifying things to enhance the amount of the customer. In 2001, Jeff Hammerbacher and DJ Patil used this term for building their data science team and defining their roles in Facebook and Linkedln. DJ Patil became the first chief data scientist of White House in 2015. Now a days data mining is widely used in different sectors like science, engineering, business and many more. Moreover, the national safety, card transaction stock market, genome sequence etc. are called the iceberg for data mining. Deep learning has become the most activetechniques. Capable of capturing enslavementsand complicatedpatterns far beyond other techniques,it'sreignitinga number ofthe most importantchallengeswithin theworldof informationmining, data science andcomputing.P2Analyze the theoretical background of data mining and identify data mining tools used in industry.Theoretical background of data mining:The most focusable thing for researching in data mining is to expand the decent procedure for different kinds of assignment. There are some reasons for giving effort in particular part of data mining. That are examining database themes or visualization, data mining expansion and user edge matters.
Basically, data mining is an applied part but it is important to know about the theoretical part of it. By studying theory of data mining we can understand the relational model of data mining. Codd’s relational model was one of the example of efficient for describing the construction of data. A common method of the theory of data mining is that it is statistics. More than it has reached on the side of relative knowledge bases texting and transferring the data.Some data mining tools in industryAt the modern time data mining is used in different sectors. For doing this process in a good way, we need to use some data mining tools. Some trendy tools are:WEKA: This tools can easily be used easily. It is a Java based tool that embraces many methods for imaging and statistical analysis clustering etc. fig:WEKAPython based Orange and NLTK: Orange is a platform which is written in python. We know that python is one of the easiest code language. That is why it is very much famous language. Orange python code text analysis, machine learning and data analysis. NLTK is also an authoritative language for data mining which is usually written in python.
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