hierarchical clustering it is another unsupervised learning algorithm that is used to group together the unlabeled data points having similar characteristics we will be discussing all these algorithms in detail in the upcoming applications of clustering we can find clustering useful in the following areasdata summarization and compressionclustering is widely used in the areas where we require data summarizationcompression and reduction as well the examples are image processing and vector quantization collaborative systems and customer segmentationsince clustering can be used to find similar products or same kind of usersit can be used in the area of collaborative systems and customer segmentation serve as key intermediate step for other data mining taskscluster analysis can generate compact summary of data for classificationtestinghypothesis generationhenceit serves as key intermediate step for other data mining tasks also trend detection in dynamic dataclustering can also be used for trend detection in dynamic data by making various clusters of similar trends social network analysisclustering can be used in social network analysis the examples are generating sequences in imagesvideos or audios biological data analysisclustering can also be used to make clusters of imagesvideos hence it can successfully be used in biological data analysis