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Chapter 27: Problem 12

How does clustering differ from classification?

Short Answer

Expert verified
Clustering and classification both are machine learning techniques, but clustering is an unsupervised learning method used to group similar data points together. On the other hand, classification is a supervised learning method used to predict the class or category of a new observation based on a training dataset with pre-defined labels.

Step by step solution

01

Understanding Clustering

Clustering is an unsupervised learning method that involves the grouping of data points. In other words, given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features.
02

Understanding Classification

Classification, on the other hand, is a supervised learning method. Supervised learning is a method in which we teach or train the machine using data which is well labelled. That implies some data is already tagged with the correct answer. It can be compared to learning which takes place in the presence of a supervisor or a teacher.
03

Comparison and Differences

1. Clustering is used when you have unlabeled data and want to create groups based on similar attributes; whereas, classification is used when you have labeled data and want to predict the labels of new data. 2. Clustering is unsupervised learning; whereas, classification is supervised learning. 3. In clustering, the output depends on the algorithm itself; whereas, in classification, the output is checked against pre-determined labels.

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Most popular questions from this chapter

What are the difficulties of mining association rules from large databases?

What is entropy and how is it used in building decision trees?

Apply the Apriori algorithm to the following data set. $$\begin{array}{ll} \text { Trans ID } & \text { Items Purchased } \\ \hline 101 & \text { milk, bread, eggs } \\ 102 & \text { milk, juice } \\ 103 & \text { juice, butter } \\ 104 & \text { milk, bread, eggs } \\ 105 & \text { coffee, eggs } \\ 106 & \text { coffee } \\ 107 & \text { coffee, juice } \\ 108 & \text { milk, bread, cookies, eggs } \\ 109 & \text { cookies, butter } \\ 110 & \text { milk, bread } \end{array}$$ The set of items is \(\\{\text { milk, bread, cookies, eggs, butter, coffee, juice }\\}\). Use 0.2 for the minimum support value.

The K-means algorithm uses a similarity metric of distance between a record and a cluster centroid. If the attributes of the records are not quantitative but categorical in nature, such as Income Level with values \\{low, medium, hight or Married with values \\{Yes, Nof or State of Residence with values \\{Alabama, Alaska, \(\ldots,\) Wyoming then the distance metric is not meaningful. Define a more suitable similarity metric that can be used for clustering data records that contain categorical data.

What are classification rules and how are decision trees related to them?
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