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Clustering News Articles In most of the previous chapters, we performed data mining knowing what we were looking for. Our use of target classes allowed us to learn how our variables model those targets during the training phase. This type of learning, where we have targets to train against, is called supervised learning. In this chapter, we consider what we do without those targets. This is unsupervised learning and is much more of an exploratory task. Rather than wanting to classify with our model, the goal in unsupervised learning is more about exploring the data to find insights. In this chapter, we look at clustering news articles to find trends and patterns in the data. We look at how we can extract data from different websites using a link aggregation website to show a variety of news stories. The key concepts covered in this chapter include: • Obtaining text from arbitrary websites • Using the reddit API to collect interesting news stories • Cluster analysis for unsupervised data mining • Extracting topics from documents • Online learning for updating a model without retraining it • Cluster ensembling to combine different models Obtaining news articles In this chapter, we will build a system that takes a live feed of news articles and groups them together, where the groups have similar topics. You could run the system over several weeks (or longer) to see how trends change over that time. [ 211 ]
Clustering News Articles Our system will start with the popular link aggregation website reddit, which stores lists of links to other websites, as well as a comments section for discussion. Links on reddit are broken into several categories of links, called subreddits. There are subreddits devoted to particular TV shows, funny images, and many other things. What we are interested in is the subreddits for news. We will use the /r/worldnews subreddit in this chapter, but the code should work with any other subreddit. In this chapter, our goal is to download popular stories, and then cluster them to see any major themes or concepts that occur. This will give us an insight into the popular focus, without having to manually analyze hundreds of individual stories. Using a Web API to get data We have used web-based APIs to extract data in several of our previous chapters. For instance, in Chapter 7, Discovering Accounts to Follow Using Graph Mining, we used Twitter's API to extract data. Collecting data is a critical part of the data mining pipeline, and web-based APIs are a fantastic way to collect data on a variety of topics. There are three things you need to consider when using a web-based API for collecting data: authorization methods, rate limiting, and API endpoints. Authorization methods allow the data provider to know who is collecting the data, in order to ensure that they are being appropriately rate-limited and that data access can be tracked. For most websites, a personal account is often enough to start collecting data, but some websites will ask you to create a formal developer account to get this access. Rate limiting is applied to data collection, particularly free services. It is important to be aware of the rules when using APIs, as they can and do change from website to website. Twitter's API limit is 180 requests per 15 minutes (depending on the particular API call). Reddit, as we will see later, allows 30 requests per minute. Other websites impose daily limits, while others limit on a per-second basis. Even within websites, there are drastic differences for different API calls. For example, Google Maps has smaller limits and different API limits per-resource, with different allowances for the number of requests per hour. If you find you are creating an app or running an experiment that needs more requests and faster responses, most API providers have commercial plans that allow for more calls. [ 212 ]
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[ 1 ] www.allitebooks.com
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Learning Data Mining with Python Co
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About the Author Robert Layton has
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Christophe Van Gysel is pursuing a
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www.allitebooks.com
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Table of Contents Preprocessing usi
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Table of Contents Chapter 7: Discov
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Table of Contents GPU optimization
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Preface If you have ever wanted to
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What you need for this book It shou
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Preface Reader feedback Feedback fr
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Getting Started with Data Mining We
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Chapter 1 In the preceding dataset,
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After you have the above "Hello, wo
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Chapter 1 Windows users may need to
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Chapter 1 The dataset we are going
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Chapter 1 As an example, we will co
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We get the names of the features fo
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Chapter 1 Two rules are near the to
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Chapter 1 The scikit-learn library
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We then iterate over all the sample
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Chapter 1 Overfitting is the proble
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Chapter 1 Summary In this chapter,
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Classifying with scikit-learn Estim
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Predicting Sports Winners with Deci
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Recommending Movies Using Affinity
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Chapter 4 The classic algorithm for
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Chapter 4 When loading the file, we
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Chapter 4 We will sample our datase
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Chapter 4 Implementation On the fir
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Chapter 4 We want to break out the
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The process starts by creating dict
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movie_name_data.columns = ["MovieID
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To do this, we will compute the tes
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Chapter 4 - Train Confidence: 1.000
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Extracting Features with Transforme
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Chapter 5 Thought should always be
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Chapter 5 Other features describe a
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Chapter 5 Similarly, we can convert
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Chapter 5 [18, 19, 20], [21, 22, 23
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Chapter 5 Next, we create our trans
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Chapter 5 This returns a different
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Also, we want to set the final colu
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Chapter 5 The downside to transform
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Chapter 5 A transformer is akin to
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We can then create an instance of t
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Chapter 5 Putting it all together N
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Social Media Insight Using Naive Ba
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Discovering Accounts to Follow Usin
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Chapter 7 Next, we will need a list
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Chapter 7 Make sure the filename is
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Chapter 7 cursor = results['next_cu
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Chapter 7 Next, we are going to rem
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Chapter 7 Creating a graph Now, we
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Chapter 7 As you can see, it is ver
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Chapter 7 Next, we will only add th
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Chapter 7 The difference in this gr
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Chapter 7 We can graph the entire s
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Chapter 7 Optimizing criteria Our a
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Chapter 7 Next, we need to get the
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Page 188 and 189: Chapter 8 The combination of an app
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Page 200 and 201: Chapter 8 This code correctly predi
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Page 204 and 205: Chapter 8 However, it isn't very go
Page 206: Chapter 8 Summary In this chapter,
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Page 217 and 218: Authorship Attribution "instead", "
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Chapter 11 Setting up the environme
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This will unzip only one Coval.otf
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Chapter 11 First we create the laye
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Chapter 11 Finally, we set the verb
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Chapter 11 Summary In this chapter,
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Working with Big Data Big data What
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Working with Big Data Governments a
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Working with Big Data We start by c
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Working with Big Data The final ste
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Working with Big Data Getting the d
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Working with Big Data If we aren't
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Working with Big Data Before we sta
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Working with Big Data The first val
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Working with Big Data This gives us
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Working with Big Data Next, we crea
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Working with Big Data Then, make a
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Working with Big Data Left-click th
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Working with Big Data The result is
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Next Steps… Extending the IPython
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Next Steps… Chapter 3: Predicting
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Next Steps… Vowpal Wabbit http://
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Next Steps… Deeper networks These
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Next Steps… Real-time clusterings
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Next Steps… More resources Kaggle
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authorship, attributing 185-188 AWS
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feature extraction about 82 common
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NetworkX about 145 defining 303 URL
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scikit-learn package references 305
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Thank you for buying Learning Data
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Learning Python Data Visualization
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