PPT-Clustering Beyond K -means

David Kauchak CS 158 Fall 2016 Administrative Final project Presentations on Tuesday 4 minute max 2 3 slides Email me by 9am on Tuesday What problem you tackled and results. Large-scale Single-pass k-Means . Clustering. Large-scale . k. -Means Clustering. Goals. Cluster very large data sets. Facilitate large nearest neighbor search. Allow very large number of clusters. Achieve good quality. Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. K. -means. David Kauchak. CS 451 – Fall 2013. Administrative. Final project. Presentations on Friday. 3 minute max. 1-2 PowerPoint slides. E-mail me by 9am on Friday. What problem you tackled and results. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Clustering Techniques and IR. Today. Clustering Problem and Applications. Clustering Methodologies and Techniques. Applications of Clustering in IR. Margareta Ackerman. Work with . Shai. Ben-David, . Simina. . Branzei. , and David . Loker. . Clustering is one of the most widely used tools for exploratory data analysis.. . Social Sciences. Biology. Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. 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 . Chapter 9 Finding Groups of Data – Clustering with k-means Objectives The ways clustering tasks differ from the classification tasks we examined previously How clustering defines a group, and how such groups are identified 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. 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. 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.. Department of Biological Sciences. National University of Singapore. http://. www.cs.ucdavis.edu. /~. koehl. /Teaching/BL5229. koehl@cs.ucdavis.edu. Clustering is a hard problem. Many possibilities; What is best clustering ?. 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.