PPT-Supervised Clustering—

Algorithms and Applications Christoph F Eick Department of Computer Science University of Houston Organization of the Talk Motivationwhy is it worthwhile generalizing machine learning techniques which are typically unsupervised to consider background information in form of class labels . Adapted from Chapter 3. Of. Lei Tang and . Huan. Liu’s . Book. Slides prepared by . Qiang. Yang, . UST, . HongKong. 1. Chapter 3, Community Detection and Mining in Social Media.  Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Hierarchical Clustering . Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. 2016. RECAP. Classification. with Incomplete Class . Hierarchies. Bhavana Dalvi. ¶. *. , Aditya Mishra. †. , and William W. Cohen. *. ¶ . Allen Institute . for . Artificial Intelligence, . * . School Of Computer Science. Omer Levy. . Ido. Dagan. Bar-. Ilan. University. Israel. Steffen Remus Chris . Biemann. Technische. . Universität. Darmstadt. Germany. Lexical Inference. Lexical Inference: Task Definition. 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 . to . LC-MS Data Analysis.  . October 7 2013. . IEEE . International Conference on Big Data 2013 (IEEE . BigData. 2013. ). Santa Clara CA. Geoffrey Fox, D. R. Mani, . Saumyadipta. . Pyne. gcf@indiana.edu. Dena B. French, . EdD. , RDN, . LD. ISPP Program Director & Experiential Coordinator. ISPP Class of 2017. Objectives. What is an ISPP?. Fontbonne’s. ISPP. Campus . “Tour”. Program overview & curriculum . 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. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Follow. up - . months. Symptom. . Burden. Score. Abed . et al. ., JAMA 2013. AF symptom . severity. after . a supervised weight loss program and in a control group . Follow. up - . months. Symptom. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. with Incomplete Class Hierarchies. Bhavana Dalvi. , Aditya Mishra, William W. Cohen. Semi-supervised Entity Classification. 2. Semi-supervised Entity Classification. Subset. 3. Disjoint. Semi-supervised Entity Classification.