10. Clustering News Articles | Learning Data Mining with Python

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Learning Data Mining with Python

By : Robert Layton

3.7 (7)

Learning Data Mining with Python

3.7 (7)

By: Robert Layton

Overview of this book

If you are a programmer who wants to get started with data mining, then this book is for you.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with Data Mining

1. Getting Started with Data Mining

Introducing data mining

Using Python and the IPython Notebook

A simple affinity analysis example

A simple classification example

What is classification?

Summary

2. Classifying with scikit-learn Estimators

2. Classifying with scikit-learn Estimators

scikit-learn estimators

Preprocessing using pipelines

Pipelines

Summary

3. Predicting Sports Winners with Decision Trees

3. Predicting Sports Winners with Decision Trees

Loading the dataset

Decision trees

Sports outcome prediction

Random forests

Summary

4. Recommending Movies Using Affinity Analysis

4. Recommending Movies Using Affinity Analysis

Affinity analysis

The movie recommendation problem

The Apriori implementation

Extracting association rules

Summary

5. Extracting Features with Transformers

5. Extracting Features with Transformers

Feature extraction

Feature selection

Feature creation

Creating your own transformer

Summary

6. Social Media Insight Using Naive Bayes

6. Social Media Insight Using Naive Bayes

Disambiguation

Text transformers

Naive Bayes

Application

Summary

7. Discovering Accounts to Follow Using Graph Mining

7. Discovering Accounts to Follow Using Graph Mining

Loading the dataset

Finding subgraphs

Summary

8. Beating CAPTCHAs with Neural Networks

8. Beating CAPTCHAs with Neural Networks

Artificial neural networks

Creating the dataset

Training and classifying

Improving accuracy using a dictionary

Summary

9. Authorship Attribution

9. Authorship Attribution

Attributing documents to authors

Function words

Support vector machines

Character n-grams

Using the Enron dataset

Summary

10. Clustering News Articles

10. Clustering News Articles

Obtaining news articles

Extracting text from arbitrary websites

Grouping news articles

Clustering ensembles

Online learning

Summary

11. Classifying Objects in Images Using Deep Learning

11. Classifying Objects in Images Using Deep Learning

Object classification

Application scenario and goals

Deep neural networks

GPU optimization

Setting up the environment

Application

Summary

12. Working with Big Data

12. Working with Big Data

Big data

Application scenario and goals

MapReduce

Application

Summary

A. Next Steps…

A. Next Steps…

Chapter 1 – Getting Started with Data Mining

Chapter 2 – Classifying with scikit-learn Estimators

Chapter 3: Predicting Sports Winners with Decision Trees

Chapter 4 – Recommending Movies Using Affinity Analysis

Chapter 5 – Extracting Features with Transformers

Chapter 6 – Social Media Insight Using Naive Bayes

Chapter 7 – Discovering Accounts to Follow Using Graph Mining

Chapter 8 – Beating CAPTCHAs with Neural Networks

Chapter 9 – Authorship Attribution

Chapter 10 – Clustering News Articles

Chapter 11: Classifying Objects in Images Using Deep Learning

Chapter 12 – Working with Big Data

More resources

Index

Index

Customer Reviews

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Clustering ensembles

In Chapter 3, Predicting Sports Winners with Decision Trees, we looked at a classification ensemble using the random forest algorithm, which is an ensemble of many low-quality, tree-based classifiers. Ensembling can also be performed using clustering algorithms. One of the key reasons for doing this is to smooth the results from many runs of an algorithm. As we saw before, the results from running k-means are varied, depending on the selection of the initial centroids. Variation can be reduced by running the algorithm many times and then combining the results.

Ensembling also reduces the effects of choosing parameters on the final result. Most clustering algorithms are quite sensitive to the parameter values chosen for the algorithm. Choosing slightly different parameters results in different clusters.

Evidence accumulation

As a basic ensemble, we can first cluster the data many times and record the labels from each run. We then record how many times each pair of samples...

Visually different images

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Tech Concepts

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Programming languages

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