we explore these tasks briefly in the following sections to get good feel of how unsupervised learning methods are used in the real world clustering clustering methods are machine learning methods that try to find patterns of similarity and relationships among data samples in our dataset and then cluster these samples into various groupssuch that each group or cluster of data samples has some similaritybased on the inherent attributes or features these methods are completely unsupervised because they try to cluster data by looking at the data features without any prior trainingsupervisionor knowledge about data attributesassociationsand relationships consider real-world problem of running multiple servers in data center and trying to analyze logs for typical issues or errors our main task is to determine the various kinds of log messages that usually occur frequently each week in simple wordswe want to group log messages into various clusters based on some inherent characteristics simple approach would be to extract features from the log messageswhich would be in textual format and apply clustering on the same and group similar log messages together based on similarity in content figure - shows how clustering would solve this problem basically we have raw log messages to start with our clustering system would employ feature extraction to extract features from text like word occurrencesphrase occurrencesand so on finallya clustering algorithm like -means or hierarchical clustering would be employed to group or cluster messages based on similarity of their inherent features figure - unsupervised learningclustering log messages it is quite clear from figure - that our systems have three distinct clusters of log messages where the first cluster depicts disk issuesthe second cluster is about memory issuesand the third cluster is about processor issues top feature words that helped in distinguishing the clusters and grouping similar data samples (logstogether are also depicted in the figure of coursesometimes some features might be present across multiple data samples hence there can be slight overlap of clusters too since this is unsupervised learning howeverthe main objective is always to create clusters such that elements of each cluster are near each other and far apart from elements of other clusters there are various types of clustering methods that can be classified under the following major approaches centroid based methods such as -means and -medoids hierarchical clustering methods such as agglomerative and divisive (ward'saffinity propagationdistribution based clustering methods such as gaussian mixture models density based methods such as dbscan and optics