Data Warehousing

and Data MiningLecture 1 Introduction

Wei Liu School of Computer

Science and Software

Engineering

Faculty of Engineering,

Computing and

Mathematics

CITS3401

CITS5504

Acknowledgement: The Lecture Slides are adapted from the original slides from Han’s textbook.

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Administrative

• Unit Coordinator & Lecturer

– Dr. Wei Liu

• Email: wei.liu@uwa.edu.au

• Office: CSSE Room 2.18

• Phone: 64883095

• The Unit Materials are for both CITS3401 and CITS5504

– CITS3401 Bachelor of Science (Data Science Major)

– CITS5504 Master of Information Technology

• Common Lecture Hours:

– TUESDAYS 10:00 – 11:45am

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CITS3401 and CITS5504

• Common Consultation Hour:– Tuesdays 2:00-3:00pm (Walk in - No appointment)

– Find me either in CSSE Room 2.18 or Lab 2.01

• Common Teaching Material– Lecture slides, lab sheets and projects

• Different websites– http://teaching.csse.uwa.edu.au/units/CITS3401

– http://teaching.csse.uwa.edu.au/units/CITS5504

• Different Lab Sessions (from Week 2 onward):– CITS3401: Tuesdays 2:00-4:00pm Dr. Syed Mohammed Shamsul Islam

(Shams)

– CITS5504: Mondays 9:00-11:00am Dr. Wei Liu

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Common Assessment Structures

• Two projects : 20% each– An analysis of a business scenario through an OLAP tool.

• We will be using an excel plug-in JEDOX for Data Warehousing Project.

– http://www.jedox.com/en/services/downloads

– An analysis of a data mining and exploration problem using WEKA.

• Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java Code

• http://www.cs.waikato.ac.nz/ml/weka/

• Mid-semester Test: 10% – at the lecture venue after the study break

• Final Examination: 50%

• Project Specifications and Instructions will be available on the course website.

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Text Book and Recommend Readings

• Course Text Book:

– Data Mining: Concepts and Techniques

• 2nd ed., Jiawei Han and Micheline Kamber- 2006

• 3rd ed., Jiawei Han and Micheline Kamber, Jian Pei -2011

– Jiawei Han‘s web page:

• http://web.engr.illinois.edu/~hanj/

• References:

– Data Mining: Methods and Techniques by, A. Shawkat Ali and

Saleh Wasimi Thomson, 2007

– Data Mining: The Textbook by, Charu C. Aggarwal, Springer,

May 2015

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Introduction to Data Mining

• Why Data Mining?

• What Is Data Mining? A Knowledge Discovery (KDD) Process

• A Multi-Dimensional View of Data Mining/ classification

– What Kinds of Data Can Be Mined?

– What Kinds of Patterns Can Be Mined?

– What Kinds of Technologies Are Used?

– What Kinds of Applications Are Targeted?

• Are all the patterns interesting?

• Integration of Data Mining System with Data Warehousing System

• Major Issues in Data Mining

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Why Data Mining?

• The Explosive Growth of Data: from terabytes to petabytes

– Data Explosion • Our capability of generating , collecting, storing and managing data has

grown tremendously in the last 50 years.

– Data collection and data availability• Automated data collection tools, database systems, Web, computerized

society

– Major sources of abundant data• Business: Web, e-commerce, transactions, stocks, …

• Science: Remote sensing, bioinformatics, scientific simulation, …

• Society and everyone: news, digital cameras, YouTube

• We are drowning in data, but starving for knowledge!

• “Necessity is the mother of invention”—Data mining—Automated and scalable analysis of massive data sets

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Potential Applications

• Data analysis and decision support

– Market analysis and management

• Target marketing, customer relationship management (CRM),

market basket analysis, cross selling, market segmentation

– Risk analysis and management

• Forecasting, customer retention, improved underwriting,

quality control, competitive analysis

– Fraud detection and detection of unusual patterns (outliers)

• Other Applications

– Text mining (news group, email, documents) and Web mining

– Stream data mining

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Example 1: Market Analysis

• Where does the data come from?– Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus

(public) lifestyle studies,

• Target marketing

– Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.

– Determine customer purchasing patterns over time

• Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association

• Customer profiling—What types of customers buy what products (clustering or classification)

• Customer requirement analysis

– Identify the best products for different groups of customers

– Predict what factors will attract new customers

• Provision of summary Information:

– Multidimensional summary reports

– Statistical summary information (data central tendency and variation)

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Example 2: Corporate Analysis and

Risk Management

• Finance planning and asset evaluation– cash flow analysis and prediction

– contingent claim analysis to evaluate assets

– cross-sectional and time series analysis (financial-ratio,trend analysis, etc.)

• Resource planning – summarize and compare the resources and spending

• Competition– monitor competitors and market directions

– group customers into classes and a class-based pricing procedure

– set pricing strategy in a highly competitive market

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Example 3. Fraud Detection and

Mining Unusual Patterns

• Approaches: Clustering & model construction for frauds, outlier analysis

• Applications: Health care, retail, credit card service, telecomm.

– Money laundering: suspicious monetary transactions

– Medical insurance:

• Professional patients, ring of doctors, and ring of references

• Unnecessary or correlated screening tests

– Telecommunications: phone-call fraud

• Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm

– Retail industry:

• Analysts estimate that 38% of retail shrink is due to dishonest employees

• Anti-terrorism:

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Evolution of Sciences

• Before 1600, empirical science

• 1600-1950s, theoretical science

– Each discipline has grown a theoretical component. Theoretical models often motivate

experiments and generalize our understanding.

• 1950s-1990s, computational science

– Over the last 50 years, most disciplines have grown a third, computational branch (e.g.

empirical, theoretical, and computational ecology, or physics, or linguistics.)

– Computational Science traditionally meant simulation. It grew out of our inability to find

closed-form solutions for complex mathematical models.

• 1990-now, data science (data-driven science)

– The flood of data from new scientific instruments and simulations

– The ability to economically store and manage petabytes of data online

– The Internet and computing Grid that makes all these archives universally accessible

– Scientific info. management, acquisition, organization, query, and visualization tasks

scale almost linearly with data volumes. Data mining is a major new challenge!

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Evolution of Database Technology

• 1960s:

– Data collection, database creation, IMS and network DBMS

• 1970s:

– Relational data model, relational DBMS implementation

• 1980s:

– RDBMS, advanced data models (extended-relational, OO, deductive, etc.)

– Application-oriented DBMS (spatial, scientific, engineering, etc.)

• 1990s:

– Data mining, data warehousing, multimedia databases, and Web databases

• 2000s

– Stream data management and mining

– Data mining and its applications

– Web technology (XML, data integration) and global information systems

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Why Data Mining

Summary:– Abundance of data and data archives are seldom visited.

– Far exceeded human ability for comprehension

– Intuitive decisions are prone to biases and errors, and is

extremely time-consuming and costly

– Data mining tools perform data analysis and uncover important

data patterns, contributing greatly to business strategies,

knowledge bases, and scientific and medical research.

Data Tombs

Nuggets of knowledge

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• Data mining (knowledge discovery from data)

– Extraction of interesting (non-trivial, implicit, previously

unknown and potentially useful) patterns or knowledge from

huge amount of data

– Data mining: a misnomer? (Knowledge Mining from data)

• Alternative names

– Knowledge discovery (mining) in databases (KDD), knowledge

extraction, data/pattern analysis, data archeology, data dredging,

information harvesting, business intelligence, etc.

• Watch out: Is everything “data mining”?

– Simple search and query processing

– (Deductive) expert systems

What is Data Mining?

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What is Data Mining?

• Tremendous amount of data (terabyte-petabyte)

• High-dimensionality and high complexity of data– Structured, un-structured, heterogeneous data

• Scalable

• Data mining involves integration of multiple disciplines: – Machine learning

– Pattern recognition

– Statistics

– Databases

– Business Intelligence

– Big data

• Efficient: Derived knowledge is new, interesting, informative and can be used for sophisticated application (decision making, process control, information management....)

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Data Mining: Confluence of Multiple

Disciplines

Data Mining

Database Technology Statistics

MachineLearning

PatternRecognition

Algorithm

OtherDisciplines

Visualization

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Steps of Knowledge Discovery

(KDD) Process

• This is a view from typical database systems and data warehousing communities

• Data mining plays an essential role in the knowledge discovery process

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

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Data Warehousing and Mining

Framework

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KDD Process: Several Key Steps

• Learning the application domain

– relevant prior knowledge and goals of application

• Creating a target data set: data selection

• Data cleaning and preprocessing: (may take 60% of effort!)

• Data reduction and transformation

– Find useful features, dimensionality/variable reduction, invariant representation

• Choosing functions of data mining

– summarization, classification, regression, association, clustering

• Choosing the mining algorithm(s)

• Data mining: search for patterns of interest

• Pattern evaluation and knowledge presentation

– visualization, transformation, removing redundant patterns, etc.

• Use of discovered knowledge

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Multi-Dimensional View of Data

Mining

• Data to be mined

– Database data (extended-relational, object-oriented, heterogeneous, legacy), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks

• Knowledge to be mined (or: Data mining functions)

– Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc.

– Descriptive vs. predictive data mining

– Multiple/integrated functions and mining at multiple levels

• Techniques utilized (methodologies)

– Data-intensive, data warehouse (OLAP), machine learning, statistics, pattern recognition, visualization, high-performance, etc.

• Applications adapted

– Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc.

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Data Mining: On What Kinds of

Data?

• Structured and semi-structured data

– Relational database/ Object-relational data

– Data Warehouse,

– Transactional Database

• Unstructured data

– Data streams and sensor data

– Text data and web data

– Time-series data, temporal data, sequence data (incl. bio-

sequences)

– Graphs, social networks and information networks

– Spatial data, spatiotemporal data and multimedia data

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Relational Database

• A relational database is a collection of tables, each of which is assigned a unique name.

• Each table consists of a set of attributes (columns or fields) and usually stores a large set of tuples (records or rows).

• Each tuple in a relational table represents an object identified by unique key and described by a set of attribute values.

• A semantic data model, such as the entity relationship data model, is often constructed for relational databases.

• An ER data model represents the database as a set of entities and their relationships.

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Relational Database

• Relational data can be accessed by database queries

written in a relational language such as SQL.

• A given query is transformed into a set of relational

operations such as join, selection and projection,

and is then optimized for efficient processing.

• Efficiency of retrieval, efficiency of update and

integrity are the key requirements of a good

relational database.

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An Example - AllElectronics

• Four relational tables: customer, item, employee and

branch.

• Each relation consists of a set of attributes.

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Example of Queries

• Show me a list of all items that were sold in the last quarter

• Show me the total sales of the last month, grouped by branch

• Which sales person has the highest amount of sales?

• How many sales transactions occurred in the month of September?

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Purpose of relational databases

• The main purpose of a relational database is to store

data correctly and retrieve data on demand.

• This type of data processing is sometime called

Online Transaction Processing (OLTP).

• Relational databases are passive data repositories in

the sense that a query only shows you what is

stored in the database, but cannot tell you much

about the meaning or trend of the data.

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Data Warehouse of AllElectronics

• A data warehouse is a repository of information collected

from multiple sources, stored under a unified schema,

and that usually resides at a single site.

• Need is to provide an analysis of the company’s sales per

item type per branch for the a specified period.

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Data Warehouse

• The data warehouse

may store a summary

of the transactions per

item type for each

store or, summarized

to a higher level, for

each sales region.

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Transactional Database

• A transactional database consists of a file where each record represents a transaction.

• Supports nested relation

• Transaction id: Items, Customer name, date…

• Sample Queries:

– Show me all the items purchased by ‘X’

– How many transactions include item number ‘Y’?

– market basket data analysis: Which items sold well together? (Frequent item set)

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Knowledge View: What Knowledge to be

mined?

• Data summary in multidimensional space

– Data cube and OLAP (On-Line Analytical Processing)

• Pattern discovery

– Mining frequent patterns, association and correlation

– Applying pattern mining in many other tasks

• Classification and predictive modelling

– Model construction based on some training examples

– Prediction of new data based on constructed models

• Cluster analysis: How to group data to form new categories?

• Outlier analysis: Discovery of anomalies and rare events

• Trend and evolution analysis

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Data Mining Function: (1)

Characterization and Discrimination

• Data can be associated with classes or concepts. ( e.g., classes of items: computer, printers concept of customers: bigSpender, budgetSpender… are the descriptions )

• Multidimensional concept description:

– Characterization: summarizing the class in general. (e.g. general specification of products whose sales increased by 10% and, ….profile of customers who spend more than $1000 a year. )

– Discrimination: comparison of target class with a contrast class.( compare the two groups of customers, such as who shop computer products regularly versus who rarely shop such products). Drilling down on dimensions such as occupation, age, etc.)

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Data Mining Function: (2)

Association and Correlation Analysis

• Frequent patterns (or frequent item_sets)

– What items are frequently purchased together ?

• Association, correlation vs. causality

– A typical association rule

• Milk Bread [0.5%, 75%] (support, confidence)

– Are strongly associated items also strongly correlated?

• How to mine such patterns and/or set rules efficiently in

large datasets? ( single or multi-dimensional

association, minimum support threshold)

• How to use such patterns for classification, clustering,

and other applications?

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Data Mining Function: (3)

Classification

• Classification and label prediction

– Construct models (functions) based on some training examples or rules….[example: kind of response (good, mild, no) in sales campaign: price, brand, category, place_made…]

– Describe and distinguish classes or concepts for future prediction

• E.g., classify countries based on (climate), or classify cars based on (gas mileage)

– Predict some unknown class labels

• Typical methods

– Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern-based classification, logistic regression, …

• Typical applications:

– Credit card fraud detection, direct marketing, classifying stars, diseases, web-pages, …

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Data Mining Function: (4) Cluster

Analysis

• Unsupervised learning (i.e., Class label is unknown)

• Group data to form new categories (i.e., clusters),

e.g., cluster houses to find distribution patterns

• Principle: Maximizing intra-class similarity &

minimizing interclass similarity

• Example: homogeneous sub-population of

AllElectronics customers (customer attributes: city,

age, income,..)

• Many methods and applications

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Data Mining Function: (5) Outlier

Analysis

• Outlier analysis

– Outlier: A data object that does not comply with the general

behavior of the data

– Most data mining methods discard outliers as noise or

exceptions.

– Noise or exception? ― One person’s garbage could be

another person’s treasure

– Methods: by product of clustering or regression analysis,

distance analysis, statistical or probability model,

– Useful in fraud detection, rare events are more interesting

– Example: By detecting a purchase of extremely large

amount for a given account number.

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Time and Ordering: Sequential

Pattern, Trend and Evolution Analysis

• Sequence, trend and evolution analysis

– Trend, time-series, and deviation analysis: e.g., regression and value prediction

– Sequential pattern mining

• e.g., first buy digital camera, then buy large SD memory cards

– Periodicity analysis (e.g., overall stock market evolution regularities or for particular companies)

– Motifs and biological sequence analysis

• Approximate and consecutive motifs

– Similarity-based analysis

• Mining data streams

– Ordered, time-varying, potentially infinite, data streams

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Structure and Network Analysis

• Graph mining

– Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments)

• Information network analysis

– Social networks: actors (objects, nodes) and relationships (edges)

• e.g., author networks in CS, terrorist networks

– Multiple heterogeneous networks

• A person could be multiple information networks: friends, family, classmates, …

– Links carry a lot of semantic information: Link mining

• Web mining

– Web is a big information network: from PageRank to Google

– Analysis of Web information networks

• Web community discovery, opinion mining, usage mining, …

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Methodology View: Confluence of

Multiple Disciplines

Data Mining

MachineLearning

Statistics

Applications

Algorithm

PatternRecognition

Distributed / cloud

computing

Visualization

Database Technology

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Why Confluence of Multiple

Disciplines?

• Tremendous amount of data

– Algorithms must be scalable to handle big data

• High-dimensionality of data

– Micro-array may have tens of thousands of dimensions

• High complexity of data

– Data streams and sensor data

– Time-series data, temporal data, sequence data

– Structure data, graphs, social and information networks

– Spatial, spatiotemporal, multimedia, text and Web data

– Software programs, scientific simulations

• New and sophisticated applications

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Application View: Diverse Applications

• Mining text data and mining the Web

– Web page classification and ranking, Weblog analysis, recommender systems, …

• Mining business data

– Transaction data, market basket analysis, fraud detection, …

• Data mining and software/system engineering e.g., mining software bugs , optimize system performance, help in computer vision

• Mining biological and medical data

– Gene, protein, microarray data, biological networks

• Mining social and information networks

– Community discovery, information propagation, …

• Invisible data mining : web search, stock market analysis

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Classification of Data Mining System

• According to the kinds of database mined:– relational, transactional, ….spatial, text, stream data….or World Wide Web

• According to the kinds of knowledge mined: – Based on mining functionalities, e.g. : characterization, discrimination,

association, ….can be multiple and/or integrated data mining…., can be distinguished based on granularity…, regular or irregular patterns(outliers) mining

• According to the techniques utilized: – degree of user interaction involved ( autonomous, interactive, query-driven),

method of analysis (machine learning, pattern recognition, statistics, neural network….), combining merits of individual aspects..

• According to the applications adapted: – Finance, Telecommunication, DNA, stock-market…all purpose data mining

system may not fit for domain specific minig.

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Summary (till this)

• Data mining: Discovering interesting patterns and knowledge

from massive amount of data

• A natural evolution of science and information technology, in

great demand, with wide applications

• A KDD process includes data cleaning, data integration, data

selection, transformation, data mining, pattern evaluation, and

knowledge presentation

• Mining can be performed in a variety of data

• Data mining functionalities: characterization, discrimination,

association, classification, clustering, trend and outlier

analysis, etc.

• Data mining technologies and applications

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Evaluation of Knowledge

• Are all mined knowledge interesting?

– One can mine tremendous amount of “patterns”

– Some may fit only certain dimension space

• time, location, …

– Some may not be representative, may be transient, …

• Evaluation of mined knowledge → directly mine only interesting knowledge?

– Descriptive vs. predictive

– Coverage

– Typicality vs. novelty

– Accuracy

– Timeliness

– …

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Are All the “Discovered” Patterns

Interesting?

• Data mining may generate thousands of patterns: Not all of them

are interesting

– Suggested approach: Human-centered, query-based, focused mining

• Interestingness measures

– A pattern is interesting if it is easily understood by humans, valid on new or

test data with some degree of certainty, potentially useful, novel, or validates

some hypothesis that a user seeks to confirm

• Objective vs. subjective interestingness measures

– Objective: based on statistics and structures of patterns, e.g., support,

confidence, etc.

– Subjective: based on user’s belief in the data, e.g., unexpectedness,

novelty, actionability, etc.

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Find All and Only Interesting

Patterns?

• Find all the interesting patterns: Completeness

– Can a data mining system find all the interesting patterns? Do we

need to find all of the interesting patterns?

– Heuristic vs. exhaustive search

– Association vs. classification vs. clustering

• Search for only interesting patterns: An optimization problem

– Can a data mining system find only the interesting patterns?

– Approaches

• First general all the patterns and then filter out the uninteresting

ones

• Generate only the interesting patterns—mining query

optimization

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Integration of Data Mining and Data

Warehousing

• Data mining systems, DBMS, Data warehouse systems coupling

– No coupling, loose-coupling, semi-tight-coupling, tight-coupling

• On-line analytical mining data

– integration of mining and OLAP technologies

• Interactive mining multi-level knowledge

– Necessity of mining knowledge and patterns at different levels of

abstraction by drilling/rolling, pivoting, slicing/dicing, etc.

• Integration of multiple mining functions

– Characterized classification, first clustering and then association

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Coupling Data Mining with DB/DW

Systems

• No coupling—flat file processing for developing efficient and effective

algorithms,… is a poor design as may spend time in preprocessing.

• Loose coupling- Fetching data from DB/DW. Mining does not explore

data structure and optimization methods provided by DB & DW.Difficult for

high scalability.

• Semi-tight coupling—enhanced DM performance

– Provide efficient implement a few data mining primitives in a DB/DW

system, e.g., sorting, indexing, aggregation, histogram analysis, multiway

join, precomputation of some statistical functions

• Tight coupling—uniform processing environment

– DM is smoothly integrated into a DB/DW system, mining query is optimized

based on mining query, indexing, query processing methods, etc.

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Major Issues in Data Mining (1)

• Mining Methodology

– Mining various and new kinds of knowledge

– Mining knowledge in multi-dimensional space at multiple level of

abstraction.

– Data mining: An interdisciplinary effort

– Boosting the power of discovery in a networked environment

– Handling noise, uncertainty, and incompleteness of data

– Pattern evaluation and pattern- or constraint-guided mining

• User Interaction

– Interactive mining

– Background knowledge (integrity constraints & deduction rules)

– Presentation and visualization of data mining results

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Major Issues in Data Mining (2)

• Efficiency and Scalability

– Efficiency and scalability of data mining algorithms

– Parallel, distributed, stream, and incremental mining methods

• Diversity of data types

– Handling complex types of data

– Mining dynamic, networked, and global data repositories

• Data mining and society

– Social impacts of data mining

– Privacy-preserving data mining

– Invisible data mining

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A Brief History of Data Mining Society

• 1989 IJCAI Workshop on Knowledge Discovery in Databases

– Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley,

1991)

• 1991-1994 Workshops on Knowledge Discovery in Databases

– Advances in Knowledge Discovery and Data Mining (U. Fayyad, G.

Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996)

• 1995-1998 International Conferences on Knowledge Discovery in

Databases and Data Mining (KDD’95-98)

– Journal of Data Mining and Knowledge Discovery (1997)

• ACM SIGKDD conferences since 1998 and SIGKDD Explorations

• More conferences on data mining

– PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM

(2001), WSDM (2008), etc.

• ACM Transactions on KDD (2007)