Jiawei Han Department of Computer Science

Slides:

Advertisements

Similar presentations

Pattern Finding and Pattern Discovery in Time Series

Advertisements

ADAPTIVE FASTEST PATH COMPUTATION ON A ROAD NETWORK: A TRAFFIC MINING APPROACH Hector Gonzalez, Jiawei Han, Xiaolei Li, Margaret Myslinska, John Paul Sondag.

Mining Compressed Frequent- Pattern Sets Dong Xin, Jiawei Han, Xifeng Yan, Hong Cheng Department of Computer Science University of Illinois at Urbana-Champaign.

1 Copyright Jiawei Han; modified by Charles Ling for CS411a/538a Data Mining and Data Warehousing  Introduction  Data warehousing and OLAP for data mining.

gSpan: Graph-based substructure pattern mining

SAK 5609 DATA MINING Prof. Madya Dr. Md. Nasir bin Sulaiman

Progress Report on Continuous Data Stream Management  Mining Frequent Itemsets over Data Streams  Music Virtual Channel Presented by: Dr. Yi-Hung Wu.

© Prentice Hall1 DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn Keogh (some slides adapted from) Margaret Dunham Dr. M.H.Dunham, Data Mining,

Continuous Data Stream Processing

Based on Slides by D. Gunopulos (UCR)

Data Mining – Intro.

Advanced Database Applications Database Indexing and Data Mining CS591-G1 -- Fall 2001 George Kollios Boston University.

DASHBOARDS Dashboard provides the managers with exactly the information they need in the correct format at the correct time. BI systems are the foundation.

OLAM and Data Mining: Concepts and Techniques. Introduction Data explosion problem: –Automated data collection tools and mature database technology lead.

Data Mining : Introduction Chapter 1. 2 Index 1. What is Data Mining? 2. Data Mining Functionalities 1. Characterization and Discrimination 2. MIning.

Data Mining Techniques

A Short Introduction to Sequential Data Mining

1 An Introduction to Data Mining Hosein Rostani Alireza Zohdi Report 1 for “advance data base” course Supervisor: Dr. Masoud Rahgozar December 2007.

Will Data Mining Change the Functions of DBMS? Jiawei Han DAIS (Data And Information Systems) Lab University of Illinois at Urbana-Champaign.

Understanding Data Analytics and Data Mining Introduction.

What Is Sequential Pattern Mining?

Chapter 1 Introduction to Data Mining

Data Mining Chapter 1 Introduction -- Basic Data Mining Tasks -- Related Concepts -- Data Mining Techniques.

Lecture 11 Sequential Pattern Mining MW 4:00PM-5:15PM Dr. Jianjun Hu CSCE822 Data Mining and Warehousing University.

Overview of CS Class Jiawei Han Department of Computer Science

Data Mining – Intro. Course Overview Spatial Databases Temporal and Spatio-Temporal Databases Multimedia Databases Data Mining.

Data Mining In contrast to the traditional (reactive) DSS tools, the data mining premise is proactive. Data mining tools automatically search the data.

1 STAT 5814 Statistical Data Mining. 2 Use of SAS Data Mining.

Group A Next Generation Information Access Group.

1 Introduction to Data Mining C hapter 1. 2 Chapter 1 Outline Chapter 1 Outline – Background –Information is Power –Knowledge is Power –Data Mining.

Introduction to Data Mining by Yen-Hsien Lee Department of Information Management College of Management National Sun Yat-Sen University March 4, 2003.

Mining Frequent Patterns. What Is Frequent Pattern Analysis? Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs.

Advanced Database Concepts

February 13, 2016 Data Mining: Concepts and Techniques 1 1 Data Mining: Concepts and Techniques These slides have been adapted from Han, J., Kamber, M.,

WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.

WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.

CS570: Data Mining Spring 2010, TT 1 – 2:15pm Li Xiong.

Knowledge Discovery in a DBMS Data Mining Computing models and finding patterns in large databases current major challenge in database systems & large.

July 7, 2016 Data Mining: Concepts and Techniques 1 1.

Data Mining - Introduction Compiled By: Umair Yaqub Lecturer Govt. Murray College Sialkot.

Data Mining Tarek Soukieh 11/18/2010. Agenda 1.The Evolution of Database Technology 2.Introduction 3.Data Preprocessing 4.OLAP vs. Data Mining 5.Data.

Frequent Pattern Mining: Current Status and Future Direction

Data Mining Functionalities

Data Mining: Concepts and Techniques (3rd ed.) — Chapter 1 —

Data Mining – Intro.

Updating SF-Tree Speaker: Ho Wai Shing.

Data Mining: Concepts and Techniques (3rd ed.) — Chapter 13 —

DATA MINING © Prentice Hall.

Association rule mining

Hidden Markov Models (HMMs)

Data warehouse & Data Mining: Concepts and Techniques

Datamining : Refers to extracting or mining knowledge from large amounts of data Applications : Market Analysis Fraud Detection Customer Retention Production.

©Jiawei Han Department of Computer Science

©Jiawei Han and Micheline Kamber

Introduction to Data Mining

Mining Dynamics of Data Streams in Multi-Dimensional Space

©Jiawei Han and Micheline Kamber

CS7280: Special Topics in Data Mining Information/Social Networks

Hidden Markov Models (HMMs)

Research Areas Christoph F. Eick

Data Warehousing and Data Mining

I don’t need a title slide for a lecture

Data Mining: Concepts and Techniques — Chapter 8 — 8

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques — Chapter 8 — 8

Multiple Sequence Alignment (I)

Data Mining: Concepts and Techniques

Online Analytical Processing Stream Data: Is It Feasible?

Data Mining: Concepts and Techniques — Chapter 8 — 8

Presentation transcript:

Data Mining: Algorithms and Principles CS512 Midterm Coverage and Review Outlines Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign www.cs.uiuc.edu/~hanj September 22, 2018 Data Mining: Pirnciples, Algorithms and Applications

Data Mining: Pirnciples, Algorithms and Applications Outline Stream Data Mining Mining time series and sequence data Graph and structured pattern mining Mining spatial, spatiotemporal and multimedia data Multi-relational and cross-database data mining September 22, 2018 Data Mining: Pirnciples, Algorithms and Applications

Data Mining: Pirnciples, Algorithms and Applications Mining Data Streams What is stream data? Why stream data mining? Stream data management systems: Issues and solutions Methods for approximate query answering Stream data cube and multidimensional OLAP analysis A stream cube architecture and implementation methods Stream frequent pattern analysis Lossy counting method for mining frequent itemsets Stream classification Decision tree induction method for dynamic data streams Stream cluster analysis K-median based method for clustering data streams CluStream method for clustering evolving data streams September 22, 2018 Data Mining: Pirnciples, Algorithms and Applications

Time-Series and Sequential Pattern Mining Regression and trend analysis Trend discovery in time-series Similarity search in time-series analysis Similarity search and subsequence matching Sequential pattern mining algorithms Sequential pattern vs. closed sequential pattern Efficient mining of sequential patterns: CloSpan vs. PrefixSpan vs. Spade vs. GSP Markov chain and hidden Markov model Markov chain models, first-order vs. higher order, and their applications Learning and prediction using HMM September 22, 2018 Data Mining: Pirnciples, Algorithms and Applications

Graph and Structured Pattern Mining Graph pattern mining and its applications Frequent subgraph mining and closed graph pattern mining The gSpan algorithm The CloseGraph algorithm Graph indexing techniques Indexing by discriminative and frequent pattern analysis The gIndex algorithm September 22, 2018 Data Mining: Pirnciples, Algorithms and Applications

Mining Spatial and Multimedia data Spatial Database Systems (SDBMS) spatial data types, queries and query processing Spatial Data Warehousing Spatial OLAP (models and implementations) Spatial Data Mining Spatial association and co-location rule mining Spatial classification and clustering Spatial outlier detection Mining multimedia databases Content-based retrieval and similarity search Progressive deepening at mining multimedia databases September 22, 2018 Data Mining: Pirnciples, Algorithms and Applications

Multi-Relational and Multi-DB Mining Classification over multiple-relations in databases Motivation and major challenges The CrossMine algorithm Major ideas: TID propagation, rule generation, look-one-ahead, negative tuple sampling Performance: reasoning on efficiency and accuracy September 22, 2018 Data Mining: Pirnciples, Algorithms and Applications