Essay About Data Miningdecember And Association Rules

Discussion 9.1 – Association Rules
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Assignment – 9.1 Bharat Surana Rajender Kumar SuranaSullivan UniversityCSC550X – Data MiningDecember 1, 2017Satellite Radio Customers. An analyst at a subscription-based satellite radio company has been given a sample of data from their customer database, with the goal of finding groups of customers that are associated with one another. The data consist of company data, together with purchased demographic data that are mapped to the company data (see Figure 13.5). The analyst decides to apply association rules to learn more about the associations between customers. Comment on this approach.[pic 1]From the above table we see that, there is no association between the rows that be found, and due to this reason association rules might not be the best approach. Also, association rules determine the association based on the item variables such as the columns in a data set. As the association rule defines associations between the purchases data or company data of a customer (Shmueli, Bruce, & Patel, 2016). Due to this, it will not help in analyzing the association in items among the rows or customers in this case. For this data, Cluster analysis may be a better approach when compared to the Associate rule, Cluster analysis is an exploratory data analysis tool and it aims at sorting distinctive objects into groups, in a certain way that there is maximal degree of association between two objects if they belong to the same group and minimal if they are not in the same group.Online Statistics Courses. Consider the data in the file CourseTopics.xls, the first few rows of which are shown in Figure 13.6. These data are for purchases of online statistics courses at statistics.com. Each row represents the courses attended by a single customer.The firm wishes to assess alternative sequencings and combinations of courses. Use association rules to analyze these data and interpret several of the resulting rules.[pic 2]XLMiner : Association RulesOutput NavigatorInputsList of RulesInputsData# Transactions in Input Data365# Columns in Input Data8# Items in Input Data8# Association Rules12Minimum Support10Minimum Confidence50.000000%Date: 03-Dec-2017 17:38:29Elapsed Times in MillisecondsAssocRules TimeReport TimeTotal32032List of RulesRule: If all Antecedent items are purchased, then with Confidence percentage Consequent items will also be purchased.Row IDConfidence %Antecedent (A)Consequent (C)Support for ASupport for CSupport for A & CLift Ratio162.5DataMining & RegressionCat Data1676103.001644737264.70588235Intro & DOESW1781112.915758896350Cat Data & RegressionDataMining2065102.807692308455.55555556DataMining & Cat DataRegression1876102.668128655554.54545455Intro & SurveySW2281122.457912458653.84615385Intro & RegressionSW2681142.42640076750Intro & DataMiningRegression2076102.401315789870Regression & SWIntro20144141.774305556966.66666667Survey & SWIntro18144121.6898148151062.5DataMining & RegressionIntro16144101.5842013891160Cat Data & RegressionIntro20144121.5208333331252.38095238DOE & SWIntro21144111.32771164