Book Image

Clojure for Machine Learning

By : Akhil Wali
Book Image

Clojure for Machine Learning

By: Akhil Wali

Overview of this book

<p>Clojure for Machine Learning is an introduction to machine learning techniques and algorithms. This book demonstrates how you can apply these techniques to real-world problems using the Clojure programming language.</p> <p>It explores many machine learning techniques and also describes how to use Clojure to build machine learning systems. This book starts off by introducing the simple machine learning problems of regression and classification. It also describes how you can implement these machine learning techniques in Clojure. The book also demonstrates several Clojure libraries, which can be useful in solving machine learning problems.</p> <p>Clojure for Machine Learning familiarizes you with several pragmatic machine learning techniques. By the end of this book, you will be fully aware of the Clojure libraries that can be used to solve a given machine learning problem.</p>

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Clojure for Machine Learning
Akhil Wali
Apr, 2014 | 292 pages
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Table of Contents (17 chapters)
Clojure for Machine Learning
Clojure for Machine Learning
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Working with Matrices
Working with Matrices
Introducing Leiningen
Representing matrices
Generating matrices
Adding matrices
Multiplying matrices
Transposing and inverting matrices
Interpolating using matrices
Summary
2
Understanding Linear Regression
Understanding Linear Regression
Understanding single-variable linear regression
Understanding gradient descent
Understanding multivariable linear regression
Understanding Ordinary Least Squares
Using linear regression for prediction
Understanding regularization
Summary
3
Categorizing Data
Categorizing Data
Understanding the binary and multiclass classification
Understanding the Bayesian classification
Using the k-nearest neighbors algorithm
Using decision trees
Summary
4
Building Neural Networks
Building Neural Networks
Understanding nonlinear regression
Representing neural networks
Understanding multilayer perceptron ANNs
Understanding the backpropagation algorithm
Understanding recurrent neural networks
Building SOMs
Summary
5
Selecting and Evaluating Data
Selecting and Evaluating Data
Understanding underfitting and overfitting
Varying the regularization parameter
Understanding learning curves
Improving a model
Using cross-validation
Building a spam classifier
Summary
6
Building Support Vector Machines
Building Support Vector Machines
Understanding large margin classification
Linear classification using SVMs
Using kernel SVMs
Summary
7
Clustering Data
Clustering Data
Using K-means clustering
Using hierarchical clustering
Using Expectation-Maximization
Using SOMs
Reducing dimensions in the data
Summary
8
Anomaly Detection and Recommendation
Anomaly Detection and Recommendation
Detecting anomalies
Building recommendation systems
Content-based filtering
Collaborative filtering
Using the Slope One algorithm
Summary
9
Large-scale Machine Learning
Large-scale Machine Learning
Using MapReduce
Querying and storing datasets
Machine learning in the cloud
Summary
References
References
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Index
Index

Using K-means clustering

The K-means clustering algorithm is a clustering technique that is based on vector quantization (for more information, refer to "Algorithm AS 136: A K-Means Clustering Algorithm"). This algorithm partitions a number of sample vectors into K clusters and hence derives its name. In this section, we will study the nature and implementation of the K-means algorithm.

Quantization, in signal processing, is the process of mapping a large set of values into a smaller set of values. For example, an analog signal can be quantized to 8 bits and the signal can be represented by 256 levels of quantization. Assuming that the bits represent values within the range of 0 to 5 volts, the 8-bit quantization allows a resolution of 5/256 volts per bit. In the context of clustering, quantization of input or output can be done for the following reasons:

  • To restrict the clustering to a finite set of clusters.

  • To accommodate a range of values in the sample data that need to have some level...

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