Therefore, it’s advised to run the K-Means algorithm multiple times before drawing inferences about the clusters. K-Means clustering algorithm instead converses on local minima, which might also correspond to the global minima in some cases but not always. For two runs of K-Mean clustering, is it expected to get the same clustering results? Clustering analysis with a single variable can be visualized with the help of a histogram. of variables/ features required to perform clustering?Īt least a single variable is required to perform clustering analysis. In this scenario, the capping and flouring of variables is the most appropriate strategy. Removal of outliers is not recommended if the data points are few in number. Which of the following is the most appropriate strategy for data cleaning before performing clustering analysis, given less than the desirable number of data points? Can decision trees be used for performing clustering?ĭecision trees (and also random forests)can also be used for clusters in the data, but clustering often generates natural clusters and is not dependent on any objective function. It can also be viewed as a regression problem for assigning a sentiment score of, say, 1 to 10 for a corresponding image, text, or speech.Īnother way of looking at sentiment analysis is to consider it using a reinforcement learning perspective where the algorithm constantly learns from the accuracy of past sentiment analysis performed to improve future performance. Sentiment Analysis is an example of:Īt the fundamental level, Sentiment analysis classifies the sentiments represented in an image, text, or speech into a set of defined sentiment classes like happy, sad, excited, positive, negative, etc. Also, a movie recommendation system can be viewed as a reinforcement learning problem where it learns from its previous recommendations and improves future recommendations. In some scenarios, this can also be approached as a classification problem for assigning the most appropriate movie class to the user of a specific group of users. Then, at a fundamental level, people in the same cluster are made similar recommendations. Generally, movie recommendation systems cluster the users in a finite number of similar groups based on their previous activities and profile. Movie recommendation systems are an example of:
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