[SOUND] Hi, in this session, we're going to discuss applications of cluster analysis. Cluster analysis has lots of applications. For example, it has been properly used as pre-processing step or intermediate step for other data mining tasks. For example, you can generate complex summary of data for classification, pattern discovery, hypothesis generation and testing and many others. And it also has been popularly used for outlier detection, because outliers can be considered those points that are far away from any cluster. Cluster analysis also has been used for data summarization, compression and reduction. For example, in im, image processing, vector quantization has been using cluster analysis quite a lot. Cluster analysis also can be used for collaborative filtering, recommendation systems or customer segmentation, because clusters can be used to find like-minded users or similar products. Cluster analysis also has been used for trend detection, for dynamic data. For example, we can cluster stream data or detecting trends and patterns in dynamic data strings. And cluster analysis also has been used for multimedia data analysis, biological data analysis and social network analysis. For example, we can use cluster clustering methods to cluster images or videos or audio clips or we can use cluster analysis on genes and protein sequences and many other interesting tasks. Thank you. [MUSIC]