## Introduction

Cluster analysis is a powerful technique used to group similar data points together based on their characteristics. It is widely used in various fields, including text mining and customer segmentation. In this coursework, we will leverage Apache Mahout, a scalable machine learning library, to perform cluster analysis on a given dataset. By employing the K-means clustering algorithm with different distance measures, we aim to identify meaningful patterns and relationships within the data.

## Problem Statement

The coursework consists of two main questions. In the first question, we will use MapReduce and Mahout on a Hadoop cluster to find descriptive statistics for the temperature of each day in a given month of the year 2007. The weather data will be obtained from the NCDC, specifically the hourly weather records for April, May, June, and July. We will compute various statistics such as the difference between the maximum and minimum wind speed, the daily minimum relative humidity, the daily mean and variance of dew point temperature, and the correlation matrix among relative humidity, wind speed, and dry bulb temperature.

In the second question, we will focus on cluster analysis using Apache Mahout. We will use either the provided dataset or a self-selected dataset of text files. The text documents will be transformed into feature vectors using the vector space model, allowing us to evaluate the similarity between data points. We will apply the K-means clustering algorithm with different distance measures (Euclidean, Manhattan, and Cosine) and determine the optimum number of clusters (K) for each distance measure. Additionally, we will plot the elbow graph for K-means clustering with the Cosine measure to identify the best value for K.

## Task

Q1.1: Calculate the Difference between Maximum and Minimum Wind Speed

Write pseudo code for the mapper function to process the weather data.

Write pseudo code for the reducer function to calculate the difference between maximum and minimum wind speed for each day.

Implement the mapper and reducer functions in Python.

Run the code on the Hadoop cluster using the selected month's weather data.

Verify the results and store the output in a file.

Q1.2: Calculate Daily Minimum Relative Humidity

Repeat steps 3 for calculating the daily minimum relative humidity.

Q1.3: Calculate Daily Mean and Variance of Dew Point Temperature

Repeat steps 3 for calculating the daily mean and variance of dew point temperature.

Q1.4: Calculate Correlation Matrix

Write pseudo code for the mapper function to process the weather data.

Write pseudo code for the reducer function to calculate the correlation matrix for relative humidity, wind speed, and dry bulb temperature.

Implement the mapper and reducer functions in Python.

Test your code using a small sample of data to ensure correctness.

Run the code on the Hadoop cluster using the selected month's weather data.

Verify the results and store the output in a file.

Q2: Cluster Analysis using Apache Mahout

Create a new folder to store your code and output files for Q2.

Prepare the dataset for cluster analysis (either using the provided text files or your own dataset).

Convert the raw text into sequence files.

Create a sparse representation of the vectors.

Initialize approximate centroids for the K-means algorithm.

Write pseudo code for the K-means algorithm with Euclidean and Manhattan distance measures.

Implement the K-means algorithm using Mahout in Python.

Run the K-means algorithm for different values of K.

Evaluate the final clustering solution.

Store the output and results in separate files.

### Analyze and Compare Results

Compare the performance of the K-means algorithm using Euclidean, Manhattan, and Cosine distance measures.

Determine the optimum number (K) of clusters for each distance measure.

Plot the elbow graph for K-means clustering with Cosine measure.

Analyze and compare the different clusters obtained with different distance measures.

## Implementation Details

For the first question, we will use MapReduce and Python to implement the mappers and reducers for computing the descriptive statistics. We will provide clear pseudo code and Python code, along with comments, to ensure the reproducibility of our results.

In the second question, we will employ Apache Mahout to perform the cluster analysis. We will follow the standard steps, including creating sequence files from raw text, generating efficient representations of vectors, initializing approximate centroids for K-means, running the K-means algorithm, and evaluating the final solution. We will compare the performance of different distance measures (Euclidean, Manhattan, and Cosine) and determine the optimal number of clusters (K).

## Experiments and Results

We will conduct a thorough analysis of the data and provide a detailed report on our experiments. The report will include a summary of the impact of parameter changes on the performance of the K-means algorithm, a comparison of different distance measures, and a discussion on the best setting for K-means clustering for the given dataset. We will evaluate the performance of the algorithm for different values of K and provide insights into the strengths and limitations of the MapReduce methodology and Hadoop's MapReduce computing engine.

If you require a solution for the Cluster Analysis using Apache Mahout coursework, our team at CodersArts is here to assist you. With our expertise in machine learning, Hadoop, and distributed computing, we can help you successfully implement the K-means clustering algorithm, analyze your data, and provide valuable insights and recommendations. Feel free to contact us via email or through our website to discuss your requirements and let us revolutionize your data analysis processes.

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