Data Mining代写 Assessment Type代写 neural networks代写

Data Mining COSC  2111/0

Data Mining代写 Assessment TypeYou can do this assignment by yourself or in a group of 2. If you are working in a group please establish a group

Assignment 2

	Assessment Type	You can do this assignment by yourself or in a group of 2. If you are working in a group please establish a group in As- signment 2 Group on Canvas. Submit online via Canvas → Assignments → Assignment 2. Marks awarded for meeting re- quirements as closely as possible. Clarifications/updates may be made via announcements/relevant discussion forums. Data Mining代写
	Due Date	End of week 11, Monday 14th October 2019, 9:00am
	Marks	50

1 Overview Data Mining代写

In this assignment you are asked to explore the use of neural networks for classification and numeric prediction. You are also asked to carry out a data mining investigation on a real-world data file. You are required to write a report on your findings. You will be assessed on methodology, analysis of results and conclusions.Data Mining代写

2 Learning Outcomes

This assessment relates to the following learning outcomes of the course.

• Demonstrate advanced knowledge of data mining concepts and
• Apply the techniques of clustering, classification, association finding, feature selec- tion and visualisation on real world data
• Applydata mining software and toolkits in a range of applications
• Set up a data mining process for an application, including data preparation, mod- elling and evaluation

3 Assignment Details Data Mining代写

3.1 Part 1: Classification with Neural Networks (15marks)

This part involves predicting the Class attribute in the following file:

chronic-kidney-disease-2019.arff

in the directory:

/KDrive/SEH/SCSIT/Students/Courses/COSC2111/DataMining/data/arff/UCI/

The main goal is to achieve the lowest classification error with the lowest amount of overfitting.Data Mining代写

For the neural network training runs build a table with the following headings:

Run No	Archi- tecture-	Param eters	Train MSE	Train Error	Epochs	Test MSE	Test Error
1	ii-hh-oo	lr=.2	0.5	30%	500	0.6	40%

1. Describe the data encoding that is required for this task. How many outputsand how many inputs will there be?
2. Develop a script to generate the necessary training, validation and test files. You might want to normalize the numeric attributes with Weka Include your data preparation script as an appendix (not part of the pagecount).
3. Determine the “analyze” strategy that you will
4. UsingJavanns carry out 5 train and rest runs for a network with 10 hidden  Comment on the variation in the training runs and the degree of overfitting.
5. Experiment with different numbers of hidden nodes. What seems to be theright number of hidden nodes for this problem?Data Mining代写
6. For 10 hidden nodes, explore different values of the learning rate. What do you conclude?
7. [Optional] Change the learning function to backprop-momentum. Exploredifferent combinations of learning rate and momentum. What do you conclude?
8. Perform a run with 10 hidden nodes and no validation data. Stop training when theMSE is no longer  Get the classification error on the training and test data. Comment on the degree of overfitting.Data Mining代写
9. Comparethe classification accuracy of the neural classifiers with the classification accuracy of Weka J48 and Report Length Up to two pages.

3.2 Part 2: Numeric Prediction with Neural Networks (10 marks)Data Mining代写

This part involves predicting the Age in the following file:

chronic-kidney-disease-2019.arff

in the directory:

/KDrive/SEH/SCSIT/Students/Courses/COSC2111/DataMining/data/arff/UCI/

The main goal is to achieve the lowest mean absolute error with the lowest amount of overfitting.

The task is to predict the value of the Age variable. Build a similar table of runs to the one in the previous question.Data Mining代写

1. Describe the data encoding that is required for this task. How many outputsand how many inputs will there be? What scaling or normalization is required?
2. Modify your script from part 1 to generate the necessary training, validationand test files. You can use Weka to normalize all of the numeric attributes except for the class, ie Age attribute. You will need to write a suitable program to scale the age to the range [0,1] and another one to reverse scale the neural net outputs to get the mean absolute  Include your data preparation script as an appendix (not part of the page count).
3. UsingJavanns carry out 5 train and test runs for a network with 5 hidden  Comment on the variation in the training runs and the degree of overfitting.

[Hint: When you are comparing the predictive accuracy of different models you don’t have to reverse scale the output.]Data Mining代写

4. Experiment with different numbers of hidden nodes. What seems to be theright number of hidden nodes for this problem?
5. For 5 hidden nodes, explore different values of the learning rate. What do you conclude?
6. [Optional] Change the learning function to backprop-momentum. Exploredifferent combinations of learning rate and momentum. What do you conclude?
7. Performa run with 5 hidden nodes and no validation  Stop training when the MSE is no longer changing. Get the error on the training and test data. Comment on the degree of overfitting.
8. Comparethe mean absolute error of the neural classifiers with the mean absolute error of Weka M5P and Report Length Up to one page.Data Mining代写

3.3 Part 3: Data Mining (25 marks) Data Mining代写

This part of the assignment is concerned with the files

portugal-students.arff portugal-students.txt which are in the directory:

/KDrive/SEH/SCSIT/Students/Courses/COSC2111/DataMining/data/arff

The file portugal-students.arff contains data about students in 2 high schools in Portugal. The file portugal-students.txt contains a description of the data.Data Mining代写

Your task is to analyse this data with appropriate classification, clustering, association finding, attribute selection and visualisation techniques selected from the Weka menus and identify any “golden nuggets” in the data. If you don’t use any of the above techniques, you need to say why.

Submit: Up to two pages that describe the strategy you adopted, your methodology, the runs you performed, any “golden nuggets” you found and your conclusions.

4 Submission Data Mining代写 Data Mining代写

Submit one pdf file. Data Mining代写

After the due date, you will have 5 business days to submit your assignment as a late submission. Late submissions will incur a penalty of 10% per day. After these five days, Canvas will be closed and you will lose ALL the assignment marks.

Assessment declaration:

When you submit work electronically, you agree to the assessment declaration –

– https://www.rmit.edu.au/students/student-essentials/assessment-and-exams/assessment/assessment-declaration Data Mining代写

5 Academic integrity and plagiarism (standard warning)

Academic integrity is about honest presentation of your academic work. It means ac- knowledging the work of others while developing your own insights, knowledge and ideas. You should take extreme care that you have:

• Acknowledged words, data, diagrams, models, frameworks and/or ideas of others you have quoted (i.e. directly copied), summarised, paraphrased, discussed or men- tioned in your assessment through the appropriate referencing methods
• Provided a reference list of the publication details so your reader can locate the source if necessary. This includes material taken from Internet sites. If you do not acknowledge the sources of your material, you may be accused of plagiarism because you have passed off the work and ideas of another person without appropriate referencing, as if they were your own.Data Mining代写

RMIT University treats plagiarism as a very serious offence constituting misconduct. Plagiarism covers a variety of inappropriate behaviours, including:

• Failure to properly document asource
• Copyrightmaterial from the internet or databases
• Collusion betweenstudents

For further information on our policies and procedures, please refer to the following:

https://www.rmit.edu.au/students/student-essentials/rights-and-responsibilities/academic-integrity.

6 Marking guidelines Data Mining代写

Factors contributing to the final mark will include the number of tasks attempted, the amount of exploration of the algorithms, methodology, logical analysis and presentation of results and conclusions.

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