1 Knowledge Discovery from DataBases (KDD) A.K.A. Data Mining & by other names as well Carlo Zaniolo UCLA CS Dept.

Slides:

Advertisements

Similar presentations

Overview of Data Mining and the KDD Process Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University.

Advertisements

Advanced Data Mining: Introduction

Data Mining Glen Shih CS157B Section 1 Dr. Sin-Min Lee April 4, 2006.

Overview of Data Mining & The Knowledge Discovery Process Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University.

Data Mining: Concepts and Techniques

Data Mining Knowledge Discovery in Databases Data 31.

Dr. Tahar Kechadi Dr. Joe Carthy

Data Mining By Archana Ketkar.

July 13, 2015ICS426: Introduction1 DATA WAREHOUSING AND DATA MINING.

Data Mining – Intro.

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

CIT 858: Data Mining and Data Warehousing Course Instructor: Bajuna Salehe Web:

Geographic Data Mining Marc van Kreveld Seminar for GIVE Block 1, 2003/2004.

TURKISH STATISTICAL INSTITUTE INFORMATION TECHNOLOGIES DEPARTMENT (Muscat, Oman) DATA MINING.

Data Mining: Concepts & Techniques. Motivation: Necessity is the Mother of Invention Data explosion problem –Automated data collection tools and mature.

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

Lingma Acheson Department of Computer and Information Science, IUPUI

Data Warehouse Fundamentals Rabie A. Ramadan, PhD 2.

10 Data Mining. What is Data Mining? “Data Mining is the process of selecting, exploring and modeling large amounts of data to uncover previously unknown.

Shilpa Seth.  What is Data Mining What is Data Mining  Applications of Data Mining Applications of Data Mining  KDD Process KDD Process  Architecture.

Data Mining. 2 Models Created by Data Mining Linear Equations Rules Clusters Graphs Tree Structures Recurrent Patterns.

Chapter 1. Introduction Motivation: Why data mining?

Data Mining Dr. Chang Liu. What is Data Mining Data mining has been known by many different terms Data mining has been known by many different terms Knowledge.

Kansas State University Department of Computing and Information Sciences CIS 830: Advanced Topics in Artificial Intelligence From Data Mining To Knowledge.

Data Mining Techniques As Tools for Analysis of Customer Behavior

Data Mining: Introduction. Why Data Mining? l The Explosive Growth of Data: from terabytes to petabytes –Data collection and data availability  Automated.

Data Mining Techniques As Tools for Analysis of Customer Behavior Lecture 2:

Chapter 1 Introduction to Data Mining

1 1 Slide Introduction to Data Mining and Business Intelligence.

DATA MINING 1. 2 Data Mining Extracting or “mining” knowledge from large amounts of data Data mining is the process of autonomously retrieving useful.

Christoph F. Eick: Introduction Knowledge Discovery and Data Mining (KDD) 1 Knowledge Discovery in Data [and Data Mining] (KDD) Let us find something interesting!

2015年10月18日星期日 2015年10月18日星期日 2015年10月18日星期日 Introduction to Data Mining 1 Chapter 1 Introduction to Data Mining Chen. Chun-Hsien Department of Information.

2015年10月22日星期四 2015年10月22日星期四 2015年10月22日星期四 Introduction to Data Mining 1 Chapter 1 Introduction to Data Mining Chen. Chun-Hsien Department of Information.

Introduction to Data-Mining Marko Grobelnik Institut Jozef Stefan.

Data Mining: Concepts and Techniques. Overview 1.Introduction 2.Data Preprocessing 3.Data Warehouse and OLAP Technology: An Introduction 4.Advanced Data.

MIS2502: Data Analytics Advanced Analytics - Introduction.

January 8, 2016Data Mining: Concepts and Techniques1 Data Mining: Trends and Applications.

Academic Year 2014 Spring Academic Year 2014 Spring.

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.,

DATA MINING It is a process of extracting interesting(non trivial, implicit, previously, unknown and useful ) information from any data repository. The.

Data Warehousing/Mining 1. 2 Chapter 1. Introduction v Motivation: Why data mining? v What is data mining? v Data Mining: On what kind of data? v Data.

CENG 514. Data mining (knowledge discovery from data) – Extraction of interesting ( non-trivial, implicit, previously unknown and potentially useful)

Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 28 Data Mining Concepts.

LECTURE 3-DATA MINING. WHY DATA MINING AND THE KDD PROCESS? 2.

2016年6月12日星期日 2016年6月12日星期日 2016年6月12日星期日 Introduction to Data Mining 1 Chapter 1 Introduction to Data Mining Chen. Chun-Hsien Department of Information.

Introduction.  Instructor: Cengiz Örencik   Course materials:  myweb.sabanciuniv.edu/cengizo/courses.

Lecture-2 Bscshelp.com.  Why Data Mining and What Kinds of Data Can Be Mined?  Potential Applications 2.

Chapter 3 Building Business Intelligence Chapter 3 DATABASES AND DATA WAREHOUSES Building Business Intelligence 6/22/2016 1Management Information Systems.

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

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

There is an inherent meaning in everything. “Signs for people who can see.”

Research in Health Informatics: Bangladesh Perspective Dr. Abu Sayed Md. Latiful Hoque Professor, Dept. of CSE, BUET Workshop on Health Data Analytics.

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

Data Mining – Intro.

Data Mining Motivation: “Necessity is the Mother of Invention”

MIS2502: Data Analytics Advanced Analytics - Introduction

MIS 451 Building Business Intelligence Systems

Data warehouse & Data Mining: Concepts and Techniques

Introduction C.Eng 714 Spring 2010.

Data Warehousing and Data Mining

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques

Data Mining Concepts and Techniques

Data Mining Techniques As Tools for Analysis of Customer Behavior

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques — Chapter 1 — — Introduction —

CSE591: Data Mining by H. Liu

Presentation transcript:

1 Knowledge Discovery from DataBases (KDD) A.K.A. Data Mining & by other names as well Carlo Zaniolo UCLA CS Dept

2 What is Data Mining? zData mining yExtraction of interesting ( non-trivial, implicit, previously unknown & potentially useful) patterns or knowledge from huge amount of data. zAlternative names yKnowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence,...

3 Why Data Mining?  Explosive growth of data available—the Big-Data Revolution xBusiness: Web, e-commerce, transactions, stocks, … xScience: Remote sensing, bioinformatics, scientific simulation, … xSociety and everyone: news, digital cameras,...  We are drowning in data -- but starving for knowledge ! yKnowledge is the key to improve your business and operations yData Mining tools and techniques: automate knowledge discovery from large data sets

4 DM Applications E.g.: Marketing products to customers: 1.Find clusters of customers who share the same characteristics: interest, income level, spending habits, etc., 2.Determine customer purchasing patterns over time 3.Cross-market analysis—Find associations/co- relations between product sales (and predict on that basis) 4.Profiling—What types of customers buy what products.

5 DM Applications: Fraud Detection and Security zApproaches: Clustering & outlier detection, looking for unusual patterns. zApplications: Health care, retail, credit card service, telecomm. yAuto insurance: ring of collisions yMoney laundering: suspicious monetary transactions yMedical insurance xProfessional patients, ring of doctors, and ring of references xUnnecessary or correlated screening tests yTelecommunications: phone-call fraud xPhone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm yAnti-terrorism

6 New Applications zSoftware Bug Mining zGraph Mining: e.g. finding social networks zWeb Mining zPersonalization and reccomendations zMining and Scientific Applications—Biology zSpatio-Temporal and GIS: yFind geographical clusters. yMine for trajectories and travel plans. zMulti Relational Data Mining yMining for knowledge and relationship from multiple tables, as in yInductive Logic Programming.

7 New Research Topics zTheoretical foundations zStatistical Data Mining zVisual Data Mining zPrivacy-Preserving Data Mining

8 A Historical Perspective 1. Machine Learning (AI) 2. Decision Support Environments: Scalability, Integration, Warehousing, OLAP (DB) 3.Statistical foundation and synergism with other disciplines—e.g., visualization. 4.Mining Streams of sensor & web data

9 Work plan zIntroduction Core Techniques: 1. Classification, 2. Association, and 3. Clustering zProcess and Systems zNew Applications and Research Directions

10 Knowledge Discovery (KDD) Process yData mining—core of knowledge discovery process Data Cleaning Data Integration Data Warehouse Task-Specific Data Data Selection & preprocessing Data Mining Pattern& Rules Useful New knowledge Data Sources: transactional & operational data Auditing