PPT-DATA MINING WITH CLUSTERING

Suresh Merugu IITR Overview Definition of Clustering Existing Clustering Methods Clustering Examples Classification Classification Examples Cluster A collection of data objects Similar to one another within the same cluster. Outline. Validating clustering results. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. April 22, 2010. Last Time. GMM Model Adaptation. MAP (Maximum A Posteriori). MLLR (Maximum Likelihood Linear Regression). UMB-. MAP. for speaker recognition. Today. Graph Based Clustering. Minimum Cut. Brendan and Yifang . April . 21 . 2015. Pre-knowledge. We define a set A, and we find the element that minimizes the error. We can think of as a sample of . Where is the point in C closest to X. . Supervised & Unsupervised Learning. Supervised learning. Classification. The number of classes and class labels of data elements in training data is known beforehand. Unsupervised learning. Clustering. algorithm and its application in single . cell . RNA-. seq. data analysis and identification of gain or loss of functions of somatic mutations . Chi Zhang, Ph.D.. Center for Computational Biology and Bioinformatics. 1. Xiaoming Gao, Emilio Ferrara, Judy . Qiu. School of Informatics and Computing. Indiana University. Outline. Background and motivation. Sequential social media stream clustering algorithm. Parallel algorithm. 12-. 1. Data mining is a rapidly growing field of business analytics focused on better understanding of characteristics and patterns among variables in large data sets.. It is used to identify and understand hidden patterns that large data sets may contain.. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. What is clustering?. Grouping set of documents into subsets or clusters.. The Goal of clustering algorithm is:. To create clusters that are coherent internally, but clearly different from each other. Prepared by David Douglas, University of Arkansas. Hosted by the University of Arkansas. 1. IBM . Clustering. Hosted by the University of Arkansas. 2. Quick Refresher. . DM used to find previously unknown meaningful patterns in data.