'Document clustering' presentation slideshows

Statistical Language Models With Embedded Latent Semantic Knowledge

Statistical Language Models With Embedded Latent Semantic Knowledge. Jerome R. Bellegarda Apple Computer, Inc. Presented by Menphis Chen. Contents. Introduction Latent Semantic Analysis General Behavior LSA Feature Space Semantic Classification N-gram + LSA Language Modeling Smoothing

690 views • 53 slides

DATA MINING Introductory

DATA MINING Introductory. Dr. Mohammed Alhaddad Collage of Information Technology King AbdulAziz University CS483. Data Mining Outline. PART I Introduction Related Concepts Data Mining Techniques PART II Classification Clustering Association Rules PART III Web Mining Spatial Mining

630 views • 39 slides

SET (8)

556 views • 35 slides

Information Retrieval (8)

470 views • 35 slides

A New Suffix Tree Similarity Measure for Document Clustering

A New Suffix Tree Similarity Measure for Document Clustering. Hung Chim, Xiaotie Deng City University of Hong Kong WWW 2007 Session: Similarity Search. Internet Database Lab., SNU Hyewon Lim. April 11, 2008. Contents. Introduction Related Work A New Suffix Tree Similarity Measure

533 views • 34 slides

Data Mining and Business Intelligence

Intelligence. Knowledge. Information. Data. Data Mining and Business Intelligence. from wikipedia. Decision Support Systems. Dashboards. Data Warehousing. Data Bases. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce

633 views • 34 slides

Data Mining: Introduction

Data Mining: Introduction. Lecture Notes for Chapter 1 Introduction to Data Mining by Minqi Zhou Room: Math Building 111 (East) Tel:021-32204750-167 Mail:mqzhou@sei.ecnu.edu.cn. Slides. http://minqizhou.wordpress.com/teaching/. Why Mine Data? Commercial Viewpoint.

489 views • 32 slides

Data Mining: Introduction

Data Mining: Introduction. Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions

435 views • 32 slides

Data Mining: Introduction

Data Mining: Introduction. Lecture Notes for Chapter 1. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful

376 views • 31 slides

Data Mining: Introduction

Data Mining: Introduction. Chapter 1 of Introduction to Data Mining by Tan, Steinbach, Kumar. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions

429 views • 31 slides

Machine Learning: Symbol-based

10d. Machine Learning: Symbol-based. More clustering examples. 10.5 Knowledge and Learning 10.6 Unsupervised Learning 10.7 Reinforcement Learning 10.8 Epilogue and References 10.9 Exercises. Additional references for the slides:

531 views • 29 slides

Introduction to Data Mining by Tan, Steinbach, Kumar

Introduction to Data Mining by Tan, Steinbach, Kumar. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful

413 views • 29 slides

Data Mining: Introduction

Data Mining: Introduction. Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions

403 views • 29 slides

Data Mining: Introduction

Data Mining: Introduction. Introduction to Data Mining by Tan, Steinbach, Kumar. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions

390 views • 28 slides

Data Mining: Introduction

Data Mining: Introduction. Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions

418 views • 27 slides

Data Mining: Introduction

Data Mining: Introduction. Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions

1.28k views • 27 slides

Bayesian Networks in Document Clustering

Bayesian Networks in Document Clustering. Slawomir Wierzchon , Mieczyslaw Klopotek Michal Draminski Krzysztof Ciesielski Mariusz Kujawiak Institute of Computer Science, Polish Academy of Sciences Warsaw.

292 views • 21 slides

Data Mining

Data Mining. Sangeeta Devadiga CS 157B, Spring 2007. Agenda. What is Data Mining? Data Mining Tasks Challenges in Data mining. What is Data Mining.

406 views • 17 slides

The 5th annual UK Workshop on Computational Intelligence London, 5-7 September 2005

The 5th annual UK Workshop on Computational Intelligence London, 5-7 September 2005. Learning Topic Hierarchies from Text Documents using a Scalable Hierarchical Fuzzy Clustering Method. E. Mendes Rodrigues and L. Sacks {mmendes, lsacks}@ee.ucl.ac.uk http://www.ee.ucl.ac.uk/~mmendes/.

295 views • 15 slides

Scatter/Gather : A Cluster Based Approach to Large Document Collections

Scatter/Gather : A Cluster Based Approach to Large Document Collections. Alyssa Katz LIS 551 March 23, 2003. Introduction. Alternate uses for document clustering Give document clustering a second chance!. Old Approach. Compare Document Clustering with Vector Space Models

367 views • 11 slides