程序代写代做代考 scheme data mining database algorithm finance Java DNA AI 1intro

1intro

COMP9318: Data Warehousing
and Data Mining

— L1: Introduction —

Chapter 1. Introduction

n Motivation: Why data mining?

n What is data mining?

n Data Mining: On what kind of data?

n Data mining functionality

n Are all the patterns interesting?

n Classification of data mining systems

n Major issues in data mining

Necessity Is the Mother of Invention
n Data explosion problem

n Automated data collection tools and mature database technology lead to
tremendous amounts of data accumulated and/or to be analyzed in
databases, data warehouses, and other information repositories

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

n Solution: Data warehousing and data mining

n Data warehousing and on-line analytical processing

n Mining interesting knowledge (rules, regularities, patterns, constraints)
from data in large databases

Who could be expected to digest millions of records, each having
tens or hundreds of fields?

Evolution of Database Technology
n 1960s:

n Data collection, database creation, IMS and network DBMS
n 1970s:

n Relational data model, relational DBMS implementation
n 1980s:

n RDBMS, advanced data models (extended-relational, OO, deductive, etc.)
n Application-oriented DBMS (spatial, scientific, engineering, etc.)

n 1990s:
n Data mining, data warehousing, multimedia databases, and Web

databases
n 2000s

n Stream data management and mining
n Data mining with a variety of applications
n Web technology and global information systems

What Is Data Mining?

n Data mining (knowledge discovery from data)

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

unknown and potentially useful) patterns or knowledge from

huge amount of data

n Data mining: a misnomer?

n Alternative names

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

extraction, data/pattern analysis, data archeology, data

dredging, information harvesting, business intelligence, etc.

n Watch out: Is everything “data mining”?

n (Deductive) query processing.

n Expert systems or small ML/statistical programs

Why Data Mining?—Potential Applications

n Data analysis and decision support
n Market analysis and management

n Target marketing, customer relationship management (CRM),
market basket analysis, cross selling, market segmentation

n Risk analysis and management

n Forecasting, customer retention, improved underwriting,
quality control, competitive analysis

n Fraud detection and detection of unusual patterns (outliers)

n Other Applications
n Text mining (news group, email, documents) and Web mining
n Stream data mining
n DNA and bio-data analysis

Market Analysis and Management

n Where does the data come from?

n Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public)
lifestyle studies

n Target marketing

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

n Determine customer purchasing patterns over time

n Cross-market analysis

n Associations/co-relations between product sales, & prediction based on such association

n Customer profiling

n What types of customers buy what products (clustering or classification)

n Customer requirement analysis

n identifying the best products for different customers

n predict what factors will attract new customers

n Provision of summary information

n multidimensional summary reports

n statistical summary information (data central tendency and variation)

Corporate Analysis & Risk Management

n Finance planning and asset evaluation
n cash flow analysis and prediction
n contingent claim analysis to evaluate assets
n cross-sectional and time series analysis (financial-ratio, trend

analysis, etc.)
n Resource planning

n summarize and compare the resources and spending
n Competition

n monitor competitors and market directions
n group customers into classes and a class-based pricing procedure
n set pricing strategy in a highly competitive market

Fraud Detection & Mining Unusual Patterns

n Approaches: Clustering & model construction for frauds, outlier analysis
n Applications: Health care, retail, credit card service, telecomm.

n Auto insurance: ring of collisions
n Money laundering: suspicious monetary transactions
n Medical insurance

n Professional patients, ring of doctors, and ring of references
n Unnecessary or correlated screening tests

n Telecommunications: phone-call fraud
n Phone call model: destination of the call, duration, time of day or

week. Analyze patterns that deviate from an expected norm
n Retail industry

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

n Anti-terrorism

Other Applications

n Sports

n IBM Advanced Scout analyzed NBA game statistics (shots blocked,

assists, and fouls) to gain competitive advantage for New York

Knicks and Miami Heat

n Astronomy

n JPL and the Palomar Observatory discovered 22 quasars with the

help of data mining

n Internet Web Surf-Aid

n IBM Surf-Aid applies data mining algorithms to Web access logs

for market-related pages to discover customer preference and

behavior pages, analyzing effectiveness of Web marketing,

improving Web site organization, etc.

Data Mining: A KDD Process

n Data mining—core of
knowledge discovery
process

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

Steps of a KDD Process

n Learning the application domain
n relevant prior knowledge and goals of application

n Creating a target data set: data selection
n Data cleaning and preprocessing: (may take 60% of effort!)
n Data reduction and transformation

n Find useful features, dimensionality/variable reduction, invariant
representation.

n Choosing functions of data mining
n summarization, classification, regression, association, clustering.

n Choosing the mining algorithm(s)
n Data mining: search for patterns of interest
n Pattern evaluation and knowledge presentation

n visualization, transformation, removing redundant patterns, etc.
n Use of discovered knowledge

Data Mining and Business Intelligence
Increasing potential
to support
business decisions End User

Business
Analyst

Data
Analyst

DBA

Making
Decisions

Data Presentation
Visualization Techniques

Data Mining
Information Discovery

Data Exploration

OLAP, MDA

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

Data Sources
Paper, Files, Information Providers, Database Systems, OLTP

Architecture: Typical Data Mining System

Data
Warehouse

Data cleaning & data integration Filtering

Databases

Database or data
warehouse server

Data mining engine

Pattern evaluation

Graphical user interface

Knowledge-base

Data Mining: On What Kinds of Data?

n Relational database
n Data warehouse
n Transactional database
n Advanced database and information repository

n Object-relational database
n Spatial and temporal data
n Time-series data
n Stream data
n Multimedia database
n Heterogeneous and legacy database
n Text databases & WWW

Data Mining Functionalities

n Concept description: Characterization and discrimination
n Generalize, summarize, and contrast data characteristics, e.g., dry

vs. wet regions

n Association (correlation and causality)
n Diaper à Beer [0.5%, 75%]

n Classification and Prediction
n Construct models (functions) that describe and distinguish classes

or concepts for future prediction
n E.g., classify countries based on climate, or classify cars based

on gas mileage
n Presentation: decision-tree, classification rule, neural network
n Predict some unknown or missing numerical values

Data Mining Functionalities (2)

n Cluster analysis
n Class label is unknown: Group data to form new classes, e.g.,

cluster houses to find distribution patterns
n Maximizing intra-class similarity & minimizing interclass similarity

n Outlier analysis
n Outlier: a data object that does not comply with the general

behavior of the data
n Noise or exception? No! useful in fraud detection, rare events

analysis
n Trend and evolution analysis

n Trend and deviation: regression analysis
n Sequential pattern mining, periodicity analysis
n Similarity-based analysis

n Other pattern-directed or statistical analyses

Are All the “Discovered” Patterns Interesting?

n Data mining may generate thousands of patterns: Not all of them
are interesting
n Suggested approach: Human-centered, query-based, focused mining

n Interestingness measures
n 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

n Objective vs. subjective interestingness measures
n Objective: based on statistics and structures of patterns, e.g., support,

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

novelty, actionability, etc.

Can We Find All and Only Interesting Patterns?

n Find all the interesting patterns: Completeness

n Can a data mining system find all the interesting patterns?

n Heuristic vs. exhaustive search

n Association vs. classification vs. clustering

n Search for only interesting patterns: An optimization problem

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

n Approaches

n First generate all the patterns and then filter out the
uninteresting ones.

n Generate only the interesting patterns—mining query
optimization

Data Mining: Confluence of Multiple Disciplines

Data Mining

Database
Systems Statistics

Other
Disciplines

Algorithm

Machine
Learning

Visualization

Data Mining: Classification Schemes

n General functionality
n Descriptive data mining
n Predictive data mining

n Different views, different classifications
n Kinds of data to be mined
n Kinds of knowledge to be discovered
n Kinds of techniques utilized
n Kinds of applications adapted

Multi-Dimensional View of Data Mining
n Data to be mined

n Relational, data warehouse, transactional, stream, object-
oriented/relational, active, spatial, time-series, text, multi-media,
heterogeneous, legacy, WWW

n Knowledge to be mined
n Characterization, discrimination, association, classification,

clustering, trend/deviation, outlier analysis, etc.
n Multiple/integrated functions and mining at multiple levels

n Techniques utilized
n Database-oriented, data warehouse (OLAP), machine learning,

statistics, visualization, etc.
n Applications adapted

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

Major Issues in Data Mining
n Mining methodology

n Mining different kinds of knowledge from diverse data types, e.g., bio, stream,
Web

n Performance: efficiency, effectiveness, and scalability
n Pattern evaluation: the interestingness problem
n Incorporation of background knowledge
n Handling noise and incomplete data
n Parallel, distributed and incremental mining methods
n Integration of the discovered knowledge with existing one: knowledge fusion

n User interaction
n Data mining query languages and ad-hoc mining
n Expression and visualization of data mining results
n Interactive mining of knowledge at multiple levels of abstraction

n Applications and social impacts
n Domain-specific data mining & invisible data mining
n Protection of data security, integrity, and privacy

Summary

n Data mining: discovering interesting patterns from large amounts of
data

n A natural evolution of database technology, in great demand, with
wide applications

n A KDD process includes data cleaning, data integration, data selection,
transformation, data mining, pattern evaluation, and knowledge
presentation

n Mining can be performed in a variety of information repositories
n Data mining functionalities: characterization, discrimination,

association, classification, clustering, outlier and trend analysis, etc.
n Data mining systems and architectures
n Major issues in data mining

A Brief History of Data Mining Society

n 1989 IJCAI Workshop on Knowledge Discovery in Databases (Piatetsky-

Shapiro)

n Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)

n 1991-1994 Workshops on Knowledge Discovery in Databases

n Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth,

and R. Uthurusamy, 1996)

n 1995-1998 International Conferences on Knowledge Discovery in Databases

and Data Mining (KDD’95-98)

n Journal of Data Mining and Knowledge Discovery (1997)

n 1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD

Explorations

n More conferences on data mining

n PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.

Where to Find References?
n Data mining and KDD

n Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.
n Journal: Data Mining and Knowledge Discovery, KDD Explorations

n Database systems
n Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA
n Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, VLDBJ, etc.

n AI & Machine Learning
n Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), etc.
n Journals: Machine Learning, Artificial Intelligence, etc.

n Statistics
n Conferences: Joint Stat. Meeting, etc.
n Journals: Annals of statistics, etc.

n Visualization
n Conference proceedings: CHI, ACM-SIGGraph, etc.
n Journals: IEEE Trans. visualization and computer graphics, etc.

Web resources:
1. DBLP
2. Google
3. Citeseer
4. DL@lib

http://sunsite.informatik.rwth-aachen.de/dblp/db/index.html
http://www.google.com/
http://citeseer.ist.psu.edu/

Recommended Reference Books
n I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java

Implementations, Morgan Kaufmann, 2001

n C. C. Aggarwal, Data Mining: The Textbook, Springer, 2015��

n J. Leskovec, A. Rajaraman, and J. Ullman, Mining of Massive Datasets (v2.1), Cambridge
University Press, 2014.

n Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, Learning From Data. AMLBook, 2012.

n J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001

n D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001

n T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data
Mining, Inference, and Prediction, Springer-Verlag, 2001

n T. M. Mitchell, Machine Learning, McGraw Hill, 1997

n P-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining,. Addison-Wesley,
2005

n S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998

Jai’s Project (COMP9318, 2016s2)

n Problem
n http://kentandlime.com.au/, a startup company helping

male customers to stay in fashion but out of the shops.
n Status-quo:

n Ask questions, and stylists makes a list of
recommended items, and send them to customers

n If happy, customers pay for the product.
n Recommendation is the key!

n Challenges
n Dirty data
n Not an easy/typical recommendation system settings
n Customer feedbacks
n Real-time recommendations

http://www.news.com.au/lifestyle/fashion/fashion-trends/fashions-most-unlikely-trend-would-
you-buy-clothes-chosen-for-you/news-story/8634b5f06f608b9f2fd7c27758f9c10a

http://kentandlime.com.au/
http://www.news.com.au/lifestyle/fashion/fashion-trends/fashions-most-unlikely-trend-would-you-buy-clothes-chosen-for-you/news-story/8634b5f06f608b9f2fd7c27758f9c10a

Solutions – Highlight

n Use domain-knowledge and quick evaluations to
guide the whole process

n Data preprocessing
n Data source: CRM (profile) + NoSQL DB (transactions)

n Missing data: e.g., due to schema changes

n Data normalization: A’s XL = B’s L

n Data noise: k-means / binning

n Data selection: remove sparse columns/rows

n Feature engineering
n weight-to-height ratio

Solutions – Highlight /2

n Product class clustering and prediction
n Collaborative filtering with smoothing and

weighting
n Content-based recommendation (solve the cold

start problem)
n Incorporate customer feedbacks
n Association rule mining:

n LSShirts_1, Shorts_2 è Socks_3
n Emsemble of the above

n Plus many engineering efforts

Results

n Test set:
n Classification rate: 74%, on par with humans

n Deployed to production on 18-24 Nov 2016:
n Customers rejecting on average 2.36 items out of a

basket of 10-12 items è (76.4%, 80.3%)
n Latency: 2.3s

n Future work identified
n e.g., seasonality

n Check Jai’s presentation slides for more details.

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