Compare collaborative-based and content based filtering

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Reference no: EM133593975

Assignment: Data Mining

Essay Questions:

Question A:

I. What is sentiment analysis, what are its different levels and approaches, and what are its applications, benefits, limitations, and challenges in the field of text mining?

II. What is web content mining, what are its different approaches, and what are its applications, benefits, limitations, and challenges in the field of text and web mining?

Question B:

I. What are the five applications of business data mining and explain the operation and function of each.

II. List and explain all 7 factors affecting the reliability of retail knowledge discovery and give solutions to each factor. List and explain the three categories of retail data mining approaches and tell where these approaches are used. Give an example of one of the approaches.

Question C:

I. List and describe the benefits of fraud detection as well as explore the possible obstacles and issues of fraud detection. Include the different approaches of fraud detection in your answer. List the main types of fraud discussed in the case study, and describe the methodologies that are currently used and being developed to detect them.

II. Describe some of the issues found with data collection and provide methods of controlling them using the three views.

Question D:

I. How is data mining used to achieve business intelligence? What is one specific example of a business intelligence application and how do companies use it to gain value? How are BI tools used to detect anomalies and fraud and how are they use to improve logistics and inventory management. How did BI tools help Jaeger recover losses from their employees.

II. How has the popularity of Data mining affected Business information and vice versa?

Question E:

I. Compare and contrast collaborative-based filtering and content based filtering. Then list and describe the 4 large challenges in web personalization.

II. Name and explain the three Text Mining methodologies used in the "Text Mining Business Policy Documents: Applied Data Science in Finance" article.

III. Discuss how the methodologies of the case study could be applied to general text mining practices, and why it's important for these methods and ideas to be shared.

Question F:

Answer this question as if you are taking this course and writing final overview of this Data mining for Business course of these below four parts:

I. What you perceive to be the highlights of what you learned in Data Mining for Business class.

II. What part(s) of the Data Mining for Business course interested/intrigued you the most and why?

III. What part(s) of the Data Mining for Business course you enjoyed the most and why?

IV. How you think Data Mining and related topics (e.g., Big Data, Text Mining, etc.) studied this semester could be used in your future career.

Reference no: EM133593975

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