CP600038E Business Intelligence Technologies Assignment
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Business Intelligence Technologies (CP600038E)
Added on 2020-04-15
CP600038E Business Intelligence Technologies Assignment
Business Intelligence Technologies (CP600038E)
Added on 2020-04-15
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Running head: CP600038E – Business Intelligence TechnologiesCP600038E – Business Intelligence TechnologiesName of the StudentName of the UniversityAuthor Note
1 CP600038E – BUSINESS INTELLIGENCE TECHNOLOGIESTable of ContentsOverview of data mining techniques..........................................................................................3Basic understanding and description of the dataset...................................................................5Three different data mining techniques applied to the dataset...................................................5Critical insight into the data analytics performed......................................................................6Decision tree...............................................................................................................................6Clustering...................................................................................................................................8Linear regression......................................................................................................................11Conclusion................................................................................................................................12References................................................................................................................................14
2 CP600038E – BUSINESS INTELLIGENCE TECHNOLOGIESOverview of data mining techniquesData mining is a process used of the extraction of the correct amount of usefulinformation from a large pool of data and transform it into useful charts and values which canbe easy for the understanding of the data (Aggarwal and Zhai 2013). The process of datamining requires a following of a sequence of steps, which includes collection of the data,extraction of the proper amount of data, analysis of the extracted data, and prepare a statisticsbased on the analysis of the data (Agneeswaran, Tonpay and Tiwary 2013). The use of thedata mining tools can be helpful for the finding of the required amount of information fromthe data set and for the easy resolving of difficult situations. The tools can also be used forthe prediction of future trends of data in the field of large business organization (Amer andGoldstein 2012). The use of data mining in the business industry has been found to have many benefits,which include the automated future data prediction of the behavior and the values of therequired field and the analysis of a large amount of data within a short period (Bockermannand Blom 2012). Apart from this, it also helps in the process of understanding hidden patternrecognition and the yielding of improved version of predictions (Mihelčić et al. 2012). The different techniques, which can be used for the process of implementation of datamining, are:1.Statistics – This form of data mining technique is the form of mathematics, whichis related to the process of data collection and descriptive form of data (Delen2012). Most of the data analyst does not consider statistical analytical techniquesto be one of the data mining techniques. However, its ability to build patterns and
3 CP600038E – BUSINESS INTELLIGENCE TECHNOLOGIESpredictive models help in the understanding of the data easily (Mohamad andTasir 2013). 2.Association Rules – This technique helps in the finding of the associationsbetween two or more than two items (Ertek, Tapucu and Arin 2013). This helps inthe understanding of the different relations, which the variables have in thedatabase. Two things are taken into concern: how frequently is the rule appliedand how much the rule is correct (Ristoski, Bizer and Paulheim 2015). 3.Clustering – This is one of the oldest techniques being used in the data miningfield. With the help of this process, the analyst is able to find the data, which aresimilar to one another and group then under a single folder (Fan and Bifet 2013).This would help in the understanding the similarities and the differences amongthe large amount of data. 4.Neural Networks – This technique is being now used by most of the people intoday’s analysis procedures (He 2013). Artificial neural networks are designedwith the help of artificial intelligence. This process consists of two parts: node andlink (Romero and Ventura 2013). 5.Visualization – This can be said to be one of the most useful technique for theunderstanding of the data pattern. This is the first technique to be used at thebeginning of the data mining process (Hofmann and Klinkenberg 2013). However,there are numerous other techniques, which can be used for the production of thepattern design of the data, but the visualization modeling can help in changing apoor group of data into a good visual (Tayel, Reif and Dengel 2013). 6.Classification – This is the most common technique of classification used for datamining, which has a set of already set out classification samples, which helps inthe process of understanding of the large set of data (Kabakchieva 2013). This
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