Contents
How clustering can be used in classification?
KMeans is a clustering algorithm which divides observations into k clusters. Since we can dictate the amount of clusters, it can be easily used in classification where we divide data into clusters which can be equal to or more than the number of classes.
Which is needed by k-means clustering?
K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible.
Can we use K-means for classification?
K-means is an unsupervised classification algorithm, also called clusterization, that groups objects into k groups based on their characteristics.
How to use clustering to improve text classification?
The plot of the story is given in the title. The genre is text classification. The main protagonists are naive-Bayes and k-means. Motivate you to try your own intuition (little crazy at the outset may be, like using clustering for classification, actually I got a scolding from one of my professors ) and convert into a formal approach
What’s the difference between clustering and classification in ML?
They appear to be a similar process as the basic difference is minute. In the case of Classification, there are predefined labels assigned to each input instances according to their properties whereas in clustering those labels are missing. Classification is used for supervised learning whereas clustering is used for unsupervised learning.
How is clustering used in a use case?
To be specific, the age-old tf-idf technique has been used for converting the text to a number. For more details you can go through Reference 5. T he intuition used was instead of using all the words, can we give the algorithm a set of words that are relatively independent of each other.
Which is an example of a clustering algorithm?
Classification examples are Logistic regression, Naive Bayes classifier, Support vector machines etc. Whereas clustering examples are k-means clustering algorithm, Fuzzy c-means clustering algorithm, Gaussian (EM) clustering algorithm etc.