CS 565留学生作业代做、代写Python/Java编程作业、代写K-means/K-means++作业、代做Python课程设计作业
CS 565 – Fall 2018 – Project 1
Problems due 11:59PM, Monday October 22. .
Instructions:
1. You will find the datasets on Piazza under resources, named wine.csv and churn.csv.
2. Please use the directory name project1 while submitting the project via gsubmit. For details, refer to Piazza
instructions.
3. The 3-page report on the project must be in .pdf format.
4. DO NOT submit the dataset along with your source code.
5. Source codes will be checked for plagiarism using automated tools.
You may use a programming language of your choice for the implementation. However, irrespective of the
programming language used, your program must accept command line arguments in the specific format described
below. The output of your program should be a .csv file with a specific format described below, for automated
grading. Please refer to the Deliverables section for further details.
General overview. In this project you will implement K-means and K-means++ and use it to analyze two
datasets.The Wine dataset is smaller and has a very clear clustering structure. The Churn dataset is somewhat
larger, has both categorical and numerical attributes. Your grade for this project will come from three components;
the quality of your code (40 pts.), the analysis of the experiments on Wine (30 pts.) and the analysis on the Churn
data (30 pts.). You will submit the source code for your implementations which we will run to test. Further, you will
submit a pdf file of no more than 3 pages describing the experiments. Exact details of these are given at the end of
this project description.
1 Wine experiments
Wine data description. In the Wine dataset you will find the chemical analysis of 178 different wines. Each line
in the data corresponds to the analysis of one type of wine. There are 12 attributes corresponding to the following
chemical components; Alcohol, Malic acid , Ash , Alcalinity of ash, Magnesium, Total phenols, Flavanoids, Non-
flavanoid phenols, Proanthocyanins, Color intensity, Hue, OD280/OD315 of diluted wines, Proline. Each value is a
real number.
Kmeans. You will implement the K-means algorithm. Your function should take 3 arguments filename, k
and init.
filename is the path to the source file. (Note, that you need to implement reading the data. There are
prewritten functions available to read csv files for both Python and Java.)
k is the number of input clusters
init takes two strings ”random” and ”k-means++”. The default should be ”random”.
The init argument specializes how the algorithm is initialized. The ”random” option initializes the cluster
centers by choosing randomly from the data points. This yields the vanilla K-means algorithm. If the input is
”k-means++” then the initial cluster centers should be chosen based on the heuristics in K-means++ .
The output will be an array, the same length as X, containing the cluster id of each item.
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Analysis of K-means experiments. In order to be able to run K-means you first need to settle on an appropriate
distance function. For this experiment you should choose the best option among the L1, L2 and L2
2 norms. You
don’t need to run experiments with the three different objective functions, but you need to explain your choice in
your write-up.
As you may have noticed, the values of the attributes in this dataset are in different ranges. So that we don’t
compare apples to oranges often it makes sense to normalize the values so that you compare them on the same
range. Explain your normalization process in your write-up.
Finally, you will need to find the optimal number k of clusters. In order to find this value of k, you may plot the
value of the cost function over a range of values of k, for both K-means and K-means++ separately. Describe
in the write-up any insights you may discover during this process.
PCA. In this exercise, we explore a popular dimensionality reduction technique known as Principal Component
Analysis. You are not required to implement the PCA algorithm. For Python, the scikit-learn module provides an
implementation of PCA
http://scikit-learn.org/stable/modules/
generated/sklearn.decomposition.PCA.html.
Use the fit transform method of the PCA implementation from the scikit-learn module and reduce the
dimensions of each wine to 2. You can also use implementations of PCA in Java. Feel free to post any good package
resources on PCA to Piazza.
Plot each wine on a 2D scatter plot using the values of 2 latent attributes found in the previous step. Furthermore,
color each data point in the scatter plot with a color corresponding to the wines cluster label from the previous
exercise. Describe your observations on this plot in your write-up.
As a sanity check, run K-means and K-means++ on the reduced dataset with the 2 latent variables. Compare
whether the cluster assignments are the same as when you ran the algorithms on the original dataset. Comment
in your write-up whether you find the outcome of this test surprising or not and why.
2 Customer Churn experiments
Customer churn data description. This dataset contains information on costumers of a large telecommunication
company. There is information on a total of 7044 costumers and 20 attributes. The attribute values may be real as
well as categorical. Notice that the first line in the data contains the attribute names. You can see this first line to
learn more about the attributes. You’ll need to cluster this dataset in to 2 clusters (i.e. the costumers who churn -
leave the company - or stay).
Data analysis. For these experiments you will reuse your code.
Here the number of clusters is given - 2. However, the data has multiple type of values; categorical values as
well as real values in various ranges. To get a meaningful clustering you first need to preprocess the data to obtain
appropriate numerical values for each category. Find ways to process the data (e.g. turn categorical data into boolean
vectors, normalize real values) for the clustering task. In your write-up explain in detail what alterations you did to
your data.
The attributes in this dataset are well-interpretable for laypersons too. Describe any pattern you may see for the
two clusters. For this data we in fact have some ground truth available - in file churn truth.csv, information
whether the customer did or did not churn. Compare this data to your clustering outcome and with help of this try to
identify outliers (e.g. people that are misclassified). This may strengthen or weaken your confidence in the patterns
you discovered. Elaborate on this as it seems appropriate.
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Deliverables. Your code should be executable from command line with first argument as the path to the .csv file,
second argument as number of clusters, and third argument as a string which can be either random or k-means++.
For example, a python source code should be executable with the command:
python source.py </path/to/churn.csv> <k> <init>
On executing this command, your program should create a file named output.csv in the same directory.
The format of output.csv is as follows; each line contains a single integer which is the cluster id of that
wine/customer.
The source code should be self-sufficient (external library dependencies will be resolved by the grader). Source
code that does not compile/execute as per the instructions or contains unresolved bugs will lose all points for the
implementation. There are points reserved for the readability of the code.
We encourage you to ask questions on Piazza. However, please do not ask syntax.
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