Data Mining Homework Week 2

Posted on January 22, 2025
Data Mining Homework Week 2

 

In an essay format answer the following questions:

In essay format answer the following questions:

After reading the chapter by Capri (2015) on manual data collection.  Answer the following questions:

1.     What were the traditional methods of data collection in the transit system?

2.     Why are the traditional methods insufficient in satisfying the requirement of data collection?

3.     Give a synopsis of the case study and your thoughts regarding the requirements of the optimization and performance measurement requirements and the impact to expensive and labor-intensive nature.

In an APA7 formatted essay answer all questions above.  There should be headings to each of the questions above as well.  Ensure there are at least three-peer reviewed sources to support your work. The paper should be at least two pages of content (this does not include the cover page or reference page).

Learning Materials

Read:

Required Books and Resources

1. Introduction to Data Mining ISBN: 9780133128901 Authors: Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar Publisher: Addison-Wesley Publication Date: 2013-01-01 Edition: 2nd ED.

2. Introduction to Data Mining ISBN: 9788131764633 Publisher: Pearson Education India Publication Date: 2020-07-31

 

Disclaimer

The assignment sample provided by Assignments Consultancy is a previously completed work for another student and contains plagiarism. It is being shared only as a reference or guideline to help you understand how to structure and approach your own assignment. We do not recommend submitting it directly as your own work. You are solely responsible for ensuring the originality and integrity of the assignment you submit, and we advise using this sample only as inspiration while adhering to your institution's academic policies.

Solution

Data gathering is essential to the transit system since it analyses user behaviour and monitors performance. The traditional methods of data gathering in the transportation system, their inherent constraints, and the need for optimization to address their costly and labour-intensive nature are all covered in Capri's (2015) chapter on manual data collection. This article offers a thorough study of the chapter by answering the questions.

Capri's case study highlights the significance of performance monitoring and optimization in a transit system beset by costly and time-consuming human data collection. Innovative approaches and cutting-edge technologies are used to overcome these obstacles. One crucial optimization method is using automated passenger counting (APC) devices, which precisely record real-time ridership data, minimize human error, and increase the sample size. A thorough understanding of passenger behaviour is provided by integrating smart card data, such as electronic ticket payment systems, enabling accurate demand forecasting and service optimization.

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