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  3. Cluster Analysis in Python
  • 1

    Introduction to Clustering

    Free

    Before you are ready to classify news articles, you need to be introduced to the basics of clustering. This chapter familiarizes you with a class of machine learning algorithms called unsupervised learning and then introduces you to clustering, one of the popular unsupervised learning algorithms. You will know about two popular clustering techniques - hierarchical clustering and k-means clustering. The chapter concludes with basic pre-processing steps before you start clustering data.

  • 2

    Hierarchical Clustering

    This chapter focuses on a popular clustering algorithm - hierarchical clustering - and its implementation in SciPy. In addition to the procedure to perform hierarchical clustering, it attempts to help you answer an important question - how many clusters are present in your data? The chapter concludes with a discussion on the limitations of hierarchical clustering and discusses considerations while using hierarchical clustering.

  • 3

    K-Means Clustering

    This chapter introduces a different clustering algorithm - k-means clustering - and its implementation in SciPy. K-means clustering overcomes the biggest drawback of hierarchical clustering that was discussed in the last chapter. As dendrograms are specific to hierarchical clustering, this chapter discusses one method to find the number of clusters before running k-means clustering. The chapter concludes with a discussion on the limitations of k-means clustering and discusses considerations while using this algorithm.

  • 4

    Clustering in Real World

    Now that you are familiar with two of the most popular clustering techniques, this chapter helps you apply this knowledge to real-world problems. The chapter first discusses the process of finding dominant colors in an image, before moving on to the problem discussed in the introduction - clustering of news articles. The chapter concludes with a discussion on clustering with multiple variables, which makes it difficult to visualize all the data.

Initializing

Exercise

Hierarchical clustering: complete method

For the third and final time, let us use the same footfall dataset and check if any changes are seen if we use a different method for clustering.

The data is stored in a pandas DataFrame, comic_con. x_scaled and y_scaled are the column names of the standardized X and Y coordinates of people at a given point in time.

Instructions

100 XP
  • Import fcluster and linkage from scipy.cluster.hierarchy.
  • Use the complete method in the .linkage() function.