Pg. 01<\/p>\n
Question nine <\/p>\n
College of Computing and Informatics<\/p>\n
Assignment<\/p>\n
Deadline: Thursday 09\/12\/2021 @ 23:59<\/p>\n
[Total Mark for this Assignment is 10]<\/p>\n
Assignment<\/p>\n
Deadline: Thursday 09\/12\/2021 @ 23:59<\/p>\n
[Total Mark for this Assignment is 10]<\/p>\n
Data Mining and Datawarehouse<\/p>\n
IT446<\/p>\n
Data Mining and Datawarehouse<\/p>\n
IT446<\/p>\n
Instructions:<\/p>\n
This Assignment report must be submitted on Blackboard (WORD format only) via the allocated folder. <\/p>\n
You are advised to make your work clear and well-presented; marks may be reduced for poor presentation<\/p>\n
Email submission will not be accepted.<\/p>\n
Late submission will result in ZERO mark.<\/p>\n
The work should be your own, copying from students or other resources will result in ZERO mark.<\/p>\n
Use Times New Roman font for all your answers. <\/p>\n
Instructions:<\/p>\n
This Assignment report must be submitted on Blackboard (WORD format only) via the allocated folder. <\/p>\n
You are advised to make your work clear and well-presented; marks may be reduced for poor presentation<\/p>\n
Email submission will not be accepted.<\/p>\n
Late submission will result in ZERO mark.<\/p>\n
The work should be your own, copying from students or other resources will result in ZERO mark.<\/p>\n
Use Times New Roman font for all your answers. <\/p>\n
Student Details:<\/p>\n
Name: ### <\/p>\n
ID: ###<\/p>\n
CRN: ###<\/p>\n
Student Details:<\/p>\n
Name: ### <\/p>\n
ID: ###<\/p>\n
CRN: ###<\/p>\n
1 Marks<\/p>\n
1 Marks<\/p>\n
Learning Outcome(s):1<\/p>\n
Explain different data mining tasks, problems and the algorithms most appropriate for addressing them<\/p>\n
Learning Outcome(s):1<\/p>\n
Explain different data mining tasks, problems and the algorithms most appropriate for addressing them<\/p>\n
Question One<\/p>\n
Select one of the datasets from UCI Machine Learning Repositories <\/p>\n
(http:\/\/archive.ics.uci.edu\/ml\/) OR ( https:\/\/www.kaggle.com\/datasets )<\/p>\n
OR use your own dataset if available.<\/p>\n
1 Marks<\/p>\n
1 Marks<\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
Question Two<\/p>\n
The dataset may follow the following requirements (Data description)<\/p>\n
Number of instances: between 300-500<\/p>\n
Number of attributes: between 10 to 15<\/p>\n
0.5 Marks<\/p>\n
0.5 Marks<\/p>\n
Learning Outcome(s):1<\/p>\n
Explain different data mining tasks, problems and the algorithms most appropriate for addressing them.<\/p>\n
Learning Outcome(s):1<\/p>\n
Explain different data mining tasks, problems and the algorithms most appropriate for addressing them.<\/p>\n
Question Three<\/p>\n
Prepare a CSV OR ARFF format data file of the data.<\/p>\n
0.5 Marks<\/p>\n
0.5 Marks<\/p>\n
Learning Outcome(s):1<\/p>\n
Explain different data mining tasks, problems and the algorithms most appropriate for addressing them.<\/p>\n
Learning Outcome(s):1<\/p>\n
Explain different data mining tasks, problems and the algorithms most appropriate for addressing them.<\/p>\n
Question Four<\/p>\n
Load the dataset in Weka or if you prefer to use any python tools such as Google Collaborate Lab https:\/\/research.google.com\/colaboratory\/<\/p>\n
2 Marks<\/p>\n
2 Marks<\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
Question Five <\/p>\n
Do a basic preprocessing to the dataset such data cleaning \/ Data reduction \/Normalization (if exist or required) etc. <\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
2 Marks<\/p>\n
2 MarksQuestion six<\/p>\n
Based on dataset run Apriori algorithm with different support and confidence values. Discuss the generated rules. <\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
1 Mark<\/p>\n
1 MarkQuestion seven<\/p>\n
Based on your dataset selection, apply SVM data mining algorithm.<\/p>\n
Provide the result and accuracies of the algorithms and discuss it with supporting screenshots. <\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
1 Mark<\/p>\n
1 MarkQuestion eight <\/p>\n
Based on your selection dataset, Apply the Decision tree data mining algorithm with different parameter setting and record the accuracies. <\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
Learning Outcome(s): 2<\/p>\n
Apply and evaluate data mining algorithms with respect to problems they are specifically designed for.<\/p>\n
1 Mark<\/p>\n
1 MarkQuestion nine<\/p>\n
Apply the K-mean algorithm on the dataset (for k=4) and study the clusters formed.<\/p>\n","protected":false},"excerpt":{"rendered":"
Pg. 01 Question nine College of Computing and Informatics Assignment Deadline: Thursday 09\/12\/2021 @ 23:59 [Total Mark for this Assignment is 10] Assignment Deadline: Thursday 09\/12\/2021 @ 23:59 [Total Mark for this Assignment is 10] Data Mining and Datawarehouse IT446 Data Mining and Datawarehouse IT446 Instructions: This Assignment report must be submitted on Blackboard (WORD […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[10],"class_list":["post-78205","post","type-post","status-publish","format-standard","hentry","category-research-paper-writing","tag-writing"],"_links":{"self":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/posts\/78205","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/comments?post=78205"}],"version-history":[{"count":0,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/posts\/78205\/revisions"}],"wp:attachment":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/media?parent=78205"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/categories?post=78205"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/tags?post=78205"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}