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Understand The Aspects of Data Mining

Added on - 13 Sep 2019

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Data Mining in Retail(with focus on e-commerce)Student Name:Student ID:Course Name:Course ID:Faculty Name:University Name:
IntroductionThe current paper will review various literatures developed by researchers from around theworld. The focus would be to understand the aspects of data mining and its close associationwith retail industry. The retail e-ecommerce will be under consideration. The current paperhas been divided into three key chapters. The first chapter will focus on understanding thevarious aspects of data mining. The second chapter will focus on understanding data miningin context of marketing. Marketing is a highly relevant field to the ecommerce business and itis suggested to understand what has been said on this area with prime focus on data mining.The third chapter discusses data mining considering the ecommerce business. In the end, aconclusive remark has been given. Data mining, if utilized effectively, is likely to improvethe businesses to greater heights.Understanding Data MiningData mining has now become talk of the town and the organizations have realized that it canbe of great advantage to them. Data mining can allow the organizations to collect and analysehuge amount of data and stay ahead of the competition (Witten et al, 2016). The importantand beneficial aspect of adoption of data mining is the availability of data, which hasincreased substantially in the past few years. Now the companies are in situation where theygenerate tons of data and have no idea on how to use those data to make informed decisionmaking (Hall et al, 2013). Moreover, the adoption of data mining should not be for the reasonthat availability of data is increased, it should be for the reason that the advancement in thetechnology such machine learning and decreased cost of computing capability has increasedthe ease with which the organizations can understand their customers, competitors, and
overall market. Data mining is closely associated with knowledge discovery in databases(Larose, 2014).According to Fayyad et al (1996), data mining refers to the one aspect of KDD wherealgorithms are used for the extraction of data patterns. The information coming out of theextraction then can be used for the identification of trends and making informed decisions.Another definition by Cabena et al (1998) states that data mining refers to the process toextract the past information that are valid and actionable and then using the same to makeorganization related decisions. According to Fabris (1998), data mining is also called as theautomated analysis which is used to identify patterns which might have lost in the fast pacedcompetitive situation. Data mining has now become a thing of necessity for the organizations.This can help them achieve the competitive advantage in the market. The aforementioneddefinitions are given in different ways, but the central idea is the extraction of informationfrom the available data and then coming up with competitively suitable decisions (Wu et al,2014). Data mining allows the organizations to focus on the information that are importantand it also improves the knowledge of the managers regarding the market.Data mining can be categorized into various aspects. The first is visualization orsegmentation which is used to group the data into specific clusters. The data in one clustershare same characteristics or trend. In clustering method, no predefined category is used. Theclusters are identified from the data itself. Another category is visualization, in which dataobserved by plotting on suitable graph (Rokach & Maimon, 2014). The dimensions can bemany instead of just two and it allows managers to identify the hidden patterns. The thirdcategory is predictive modelling which is used for the prediction of particular attribute. Thiscategory is of high use in the industry where understanding the future trend is crucial forbusiness operation such as target marketing, customer movement, and others. The fourthcategory of data mining is link analysis, which is used to identify possible links between
various data sets (Roiger, 2017). The fifth category is deviation detection, in which outliersare detected. The researchers state that the identification of major deviations allowsunderstanding the patterns that are telling some different story than usual (Cabena et al, 1998;Yoon, 1999). The sixth category is dependency modelling, which is used to understanddependencies among the test variables. The seventh category is data summarization, which isused to get snapshot of the entire database. In the data summary, two forms are used, first isthe horizontal and another is vertical. The horizontal is used to bring out the summary of thedata subsets. On the other hand, the vertical form is used to understand the relation among thefield (Roiger, 2017).Data mining can be used in various ways in the market. There are various fields where datamining is under use with appreciable level of success such as medical, detecting fraud, andothers. One of the interesting fields where the techniques of data mining can be utilized ismarketing (Witten et al, 2016). This will allow the companies to identify their target marketfrom vast amount of data. Along with that, they will be able to understand the behaviouralpattern or characteristics of their customers and then can make more realistic decisions. Asstated earlier that the paper is focused on data mining in retail e-commerce, the literaturesassociated with it will be assessed. However, prior to moving to it, it is important tounderstand that how the data mining can be used to manage the marketing which is hugelyassociated with the e-commerce management. The next chapter addresses the same.Data Mining and MarketingMarketing is the important aspect that is closely associated with success in e-commercebusiness. It improves the ability of the organizations in prospecting the right customers. Datamining allows the businesses in understanding the attributes that are necessary for being agood prospective customer. Data mining also allows businesses in identifying the channel
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