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. Hongning Wang. CS@UVa. Today’s lecture. k. -means clustering . A typical . partitional. . clustering . algorithm. Convergence property. Expectation Maximization algorithm. Gaussian mixture model. . Clustering for . Market Segmentation . Presentation by Mike Calder . Clustering. Used for market segmentation. Researchers want to find groups that can be targeted with the same marketing strategy. Given data of which users click on certain adds, derive discriminative clusters. : Distributed Co-clustering with Map-Reduce. S. Papadimitriou, J. Sun. IBM T.J. Watson Research Center. Speaker:. 0356169. 吳宏君. 0350741. . 陳威遠. 0356042 . 洪浩哲. Outline. Introduction. Data Mining and Machine Learning are Ubiquitous!. Netflix. Amazon. Wal-Mart. Algorithmic Trading/High Frequency Trading. Banks (. Segmint. ). Google/Yahoo/Microsoft/IBM. CRM/Consumer Behavior Profiling. Cluster Analysis. Padhraic. Smyth. Department of Computer Science. Bren School of Information and Computer Sciences. University of California, Irvine. . Announcements. Assignment 1. Questions?. Due Wednesday, hardcopy in class, code to EEE. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Emre Eftelioglu. 1. What is Knowledge Discovery in Databases?. Data mining is actually one step of a larger process known as . knowledge discovery in databases. (KDD).. The KDD process model consists of six phases. Another Introduction to Data Mining. Course Information. 2. Knowledge Discovery in Data [and Data Mining] (KDD). Let us find something interesting!. Definition. := . “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” . Chapter 10. . Cluster Analysis: Basic Concepts and . Methods. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign, 2106. 1. Chapter 10. . Cluster Analysis: Basic Concepts and Methods. Cluster Analysis: An Introduction. 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.. in Robotics Engineering. Blink . Sakulkueakulsuk. D. . Wilking. , and T. . Rofer. , . Realtime. Object Recognition . Using Decision . Tree . Learning, 2005. . http. ://. www.informatik.uni-bremen.de/kogrob/papers/rc05-objectrecognition.pd. Ruizhu. Yang. 04/25/2014. References. Otari. G V, . Kulkarni. R V. A Review of Application of Data Mining in Earthquake Prediction[J]. . 2012.. Dzwinel. . W, Yuen D A, . Boryczko. K, et al. Cluster analysis, data-mining, multi-dimensional visualization of earthquakes over space, time and feature space[C]//Nonlinear Proc. in . 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. Another Introduction to Data Mining. Course Information. 2. Knowledge Discovery in Data [and Data Mining] (KDD). Let us find something interesting!. Definition. := . “KDD is the non-trivial process of identifying valid, novel, potentially...