Giáo Trình Khai pha dữ liệu

Tổng quan về công nghệ cơ sởdữ liệu „ Model: “a representation of something, either as a physical object which is usually smaller than the real object, or as a simple Æ Mô hình hóa dữ

1

Tài liệu tham khảo

Techniques”, Second Edition, Morgan Kaufmann Publishers, 2006

‡ [2] David Hand, Heikki Mannila, Padhraic Smyth, “Principles of Data

Mining”, MIT Press, 2001

Techniques”, Springer-Verlag, 2008

Methodology, Techniques, and Applications”, Springer-Verlag, 2006

‡ [5] Hillol Kargupta, Jiawei Han, Philip S Yu, Rajeev Motwani, and

Vipin Kumar, “Next Generation of Data Mining”, Taylor & Francis

Group, LLC, 2009

& Sons, Inc, 2006

‡ [7] Ian H.Witten, Eibe Frank, “Data mining : practical machine

learning tools and techniques”, Second Edition, Elsevier Inc, 2005

‡ [8] Florent Messeglia, Pascal Poncelet & Maguelonne Teisseire,

“Successes and new directions in data mining”, IGI Global, 2008

Discovery Handbook”, Second Edition, Springer Science + Business

Nội dung

‡ Chương 3: Hồi qui dữ liệu

‡ Chương 7: Khai phá dữ liệu v à công nghệ cơ

sở dữ liệu

dữ liệu

Chương 7: Khai phá dữ liệu và

công nghệ cơ sở dữ liệu

‡ 7.1 Tổng quan về công nghệ cơ sở dữ liệu

‡ 7.2 Khả năng hỗ trợ khai phá dữ liệu của

công nghệ cơ sở dữ liệu

‡ 7.3 Các ngôn ngữ truy vấn dành cho khai

phá dữ liệu

‡ 7.4 Hỗ trợ của các DBMS ngày nay dành

cho khai phá dữ liệu

‡ 7.5 Tóm tắt

7.0 Tình huống 1

Người đang sử dụng thẻ ID = 1234 thật

sự là chủ nhân của thẻ hay là một tên trộm?

7.0 Tình huống 3

Ngày mai cổ phiếu STB sẽ tăng???

7.0 Tình huống 4

Không (97%)

… 3.0

2.0 47

5.5 82

2007

Có (90%)

… 7.5

9.5 24

2006

Có (80%)

… 6.0

7.0 90

2005

Không

… 3.5

5.5 8

2004

… 14

3 2 1

5.0 2004

Không

… 2.5

4.0 2004

Có

… 8.0

6.5 2004

Có

… 8.5

9.0 2004

TốtNghiệp

… MônHọc2

MônHọc1 Khóa

Làm sao xác định được khả năng tốt nghiệp của

7.0 Tình huống …

We are data rich, but information poor.

“Necessity is the mother of invention” - Plato

7.1 Tổng quan về công nghệ cơ sở

dữ liệu

quá trình khai phá dữ liệu (tóm tắt từ

Chương 1)

„ Bắt nguồn từ yêu cầu ứng dụng thực tiễn

„ Dữ liệu thật/dữ liệu nhân tạo từ mô phỏng

„ Cấu trúc từ đơn giản đến phức tạp

„ Lượng dữ liệu lớn, biến động nhiều

„ Lưu trữ lâu dài/lưu trữ tạm thời

Æ Quản lý và tận dụng hiệu quả

trình khai phá cụ thể

thông số

„ Lưu trữ lâu dài/lưu trữ tạm thời

Æ Quản lý và tận dụng hiệu quả

7.1 Tổng quan về công nghệ cơ sở

dữ liệu

„ Model: “a representation of something,

either as a physical object which is usually

smaller than the real object, or as a simple

Æ Mô hình hóa dữ liệu cho quá trình khai phá

Æ Mô hình hóa kết quả từ quá trình khai phá

7.1 Tổng quan về công nghệ cơ sở

dữ liệu

Simple Data without Queries

Simple Data with Queries

Complex Data without Queries

Complex Data with Queries

Video, Audio, Image, Text, 3D Graphical Data, etc.

IV

7.1 Tổng quan về công nghệ cơ sở

dữ liệu

File Systems Relational DB Systems

Object Relational DB Systems Object (Oriented) DB Systems

Simple Data

Complex Data

Source: M Stonebraker, P Brown with D Moore, Object-Relational DBMS’s – Tracking the Next

5NF relations 1NF relations

No Object behaviors

Richer OCL expressions

Fewer Constraints

Relationship type Relationship type

Relationship type Relationships

Identifier OID (implicit)

Key attribute Object identity

Attribute

-Attribute Object attribute

-

-Weak entity type

Set of dependent

objects

Object type Class

Entity type Set of objects of

interest

NIAM/ORM UML

ERM Conceptual Data Model

7.1 Tổng quan về công nghệ cơ sở

dữ liệu

‡ Mô hình hóa luận lý dữ liệu cho quá trình khai phá

Methods calling Logical pointer REF

(system-generated)

OID (system-generated) Fully encapsulated object

with atomic/non-atomic attributes

Object

SQL:3, SQL:99, SQL:2003, OQL

Foreign key (attribute values)/logical pointer REF (system- generated)

Primary key (attribute values) / OID (ROWID, REFC) (system- generated)

Relation/un-encapsulated object with atomic/non- atomic attributes

Object

Relational

Nested relational algebra with nest/unnest operations

Foreign key (attribute values)

Primary key (attribute values)

Nested relation with nested relation attributes

Nested

Relational

Relational algebra, tuple relational calculus, SQL:89, SQL:92

Foreign key (attribute values)

Primary key (attribute values)

Relation with atomic attributes

Relational

Language Referential Constraint

Identity Key Construct

Data Model

‡ “A data warehouse is a subject-oriented oriented, integrated integrated, nonvolatile, and nonvolatile

time-variant variant collection of data in support of management’s decisions.”

‡ UML conceptual model

‡ Star (relational)/multidimensional model

Figure 2.5 The structure of the

data warehouse.

Source: W.H Inmon Building the

data warehouse, 3 rd Edition, John

Wiley & Sons, Inc., 2001.

7.1 Tổng quan về công nghệ cơ sở

dữ liệu

Figure 1 Decision support system architecture, which consists of three principal components: a data

warehouse server, analysis and data mining tools, and data warehouse back-end tools.

‡ Large collection of discovered knowledge

so-called pattern management system just like data by a defined/developed/used DBMS

‡ Architectural issues

‡ Representation constructs: Pattern type, Pattern, Class

‡ Implicit constraints: Pattern-Pattern type, Pattern-Class, Pattern-Pattern type

ƒ Specialization, composition, refinement

[108] S Rizzi, E Bertino, B Catania, M Golfarelli, M Halkidi, M Terrovitis, P Vassiliadis, M Vazirginannis, E

Reference architecture

for a pattern base

management system

using the logical model

Source: S Rizzi, E Bertino,

B Catania, M Golfarelli, M

Halkidi, M Terrovitis, P

Vassiliadis, M Vazirginannis,

E Vrachnos Towards a logical

model for patterns In

Proceedings of the ER 2003,

LNCS 2813, pp 77-90, 2003.

[3-2006] (language and system development – PhD thesis)

[87-2007] (Interoperability issues + support for application programs + driver development)

[73-2008] (summary)

Related Works - [108-2003]Æ [105-2007]

[108-2003] (architectural issues + representational constructs + pattern relationships)

[12-2004, 2007] (formal definition, pattern warehouse, query types, predicates and operators)

[105-2007] (more operators on pattern warehouse + indexing techniques – PhD thesis)

Related Works - [108-2003]Æ [101-2009]

[108-2003]

[99-2007] (model extension with superclass,

ontology for knowledge evaluation of

association rules and queries)

[100-2008] (pattern comparison methods for clustering)

[101-2009] (pattern comparison for crisp/fuzzy clustering, open

source prototype development (PatternMiner) – PhD thesis)

[98-2005] (Database approach: relational,

object relational, and XML-based databases)

Related works

‡ 4 B Catania, A Maddalena, M Mazza, E Bertino, S Rizzi A framework for data mining pattern

management In Proceedings of PKDD 2004, LNAI 3202, pp 87-98, 2004.

‡ 97. B Catania, A Maddalena Pattern Management: Practice and Challenges In Processing and

Managing Complex Data for Decision Support, J Darmont, O Boussaid (eds.), Idea Group

Publishing, 2006.

‡ 73 B Catania Towards effective solutions for pattern management International Journal of

Computer Science and Applications, Vol 5(3), 2008, 36-45.

‡ 98 E Kotsifakos, I Ntoutsi, Y Theodoridis Database support for data mining patterns In

Proceedings of the 10th Panhellenic Conference on Informatics (PCI’05), Advances in Informatics

– Springer-Verlag LNCS 3746, 2005.

‡ 99 E.E Kotsifakos, G Marketos, Y Theodoridis A framework for integrating ontologies and

pattern-bases Data Mining with Ontologies: Implementations, Findings, and Frameworks, H.O

Nigro, S G Cisaro, D Xodo (eds.), Chapter 12, IDEA Group, 2007.

‡ 100 E.E Kotsifakos, I Ntoutsi, Y Vrahoritis, Y Theodoridis PATTERN-MINER: Integrated

management and mining over data mining models (Demo) In Proceedings of the 14th ACM

SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08), 2008.

‡ 101 E.E Kotsifakos Pattern representation and management techniques – The PBMS concept

PhD Thesis, Department of Informatics, University of Piraeus, 2009.

‡ 3. A Maddalena A unified framework for heterogeneous pattern management PhD thesis in

Computer Science, University of Genova, April 2006.

‡ 87 A Maddalena, B Catania Towards an interoperable solution for pattern management In

Proceedings of VLDB’07, 2007.

‡ 106. R Meo, G Psaila An XML-based database for knowledge discovery In Proceedings of the

EDBT 2006 Workshops, LNCS 4254, pp 814-828, 2006.

‡ 108 S Rizzi, E Bertino, B Catania, M Golfarelli, M Halkidi, M Terrovitis, P Vassiliadis, M

Vazirginannis, E Vrachnos Towards a logical model for patterns In Proceedings of the ER 2003,

LNCS 2813, pp 77-90, 2003.

‡ 105 M Terrovitis Modelling and operational issues for pattern base management systems PhD

Thesis, Computer Science Division, School of Electrical and Computer Engineering, National

Technical University of Athens, 2007.

‡ 12 M Terrovitis, P Vassiliadis, S Skiadopoulos, E Bertino, B Catania, A Maddalena, S Rizzi

Modeling and language support for the management of pattern-bases Data & Knowledge

7.2 Khả năng hỗ trợ khai phá dữ

liệu của công nghệ cơ sở dữ liệu

‡ Từ yêu cầu tri thức trong dữ liệu thu thập

được ngày nay đến yêu cầu dành cho quá

trình khai phá dữ liệu

‡ Từ yêu cầu của quá trình khai phá dữ liệu đến

yêu cầu dành cho công nghệ cơ sở dữ liệu

Æ conventional DBMS, in-memory DBMS,

column-oriented DBMS, IR + DBMS, semantic

technologies + DBMS, service-oriented DBMS,

…

7.3 Các ngôn ngữ truy vấn dành

cho khai phá dữ liệu

‡ A data mining query language

„ select the data to be mined and pre-process

these data,

„ specify the kind of patterns to be mined,

item hierarchies when mining generalized

association rules),

„ define the constraints on the desired patterns ,

„ post-process extracted patterns

Source: J-F Boulicaut, C Masson, Data Mining Query Languages, Chapter 1 in: The Data

Mining and Knowledge Discovery Handbook, O Maimon and L Rokach (Eds.), Springer,

2005, pp 715-727.

7.3 Các ngôn ngữ truy vấn dành

cho khai phá dữ liệu

‡ Proposals for association rule mining

„ MSQL (Imielinski and Virmani, 1999) at the

Rutgers University

„ MINE RULE (Meo et al., 1998) at the University of

Torino and the Politecnico di Milano

„ DMQL (Han et al., 1996) at the Simon Fraser

University

„ OLE DB for DM by Microsoft Corporation (Netz et

al., 2000)

Source: J-F Boulicaut, C Masson, Data Mining Query Languages, Chapter 1 in: The Data

Mining and Knowledge Discovery Handbook, O Maimon and L Rokach (Eds.), Springer,

7.3 Các ngôn ngữ truy vấn dành

cho khai phá dữ liệu

‡ MSQL (Imielinski and Virmani, 1999) at the Rutgers

University

Source: J-F Boulicaut, C Masson, Data Mining Query Languages, Chapter 1 in: The Data

Mining and Knowledge Discovery Handbook, O Maimon and L Rokach (Eds.), Springer,

2005, pp 715-727.

Inductive queries to mine rules

Post-processing queries over a materialized collection of rules

7.3 Các ngôn ngữ truy vấn dành

cho khai phá dữ liệu

‡ MINE RULE (Meo et al., 1998) at the University of

Torino and the Politecnico di Milano

Source: J-F Boulicaut, C Masson, Data Mining Query Languages, Chapter 1 in: The Data Mining and

7.3 Các ngôn ngữ truy vấn dành

cho khai phá dữ liệu

‡ DMQL (Han et al., 1996) at the Simon Fraser University

Source: J-F Boulicaut, C Masson, Data Mining Query Languages, Chapter 1 in: The Data Mining and

Knowledge Discovery Handbook, O Maimon and L Rokach (Eds.), Springer, 2005, pp 715-727.

7.3 Các ngôn ngữ truy vấn dành

cho khai phá dữ liệu

‡ OLE DB for DM by Microsoft Corporation

(Netz et al., 2000)

Source: J-F Boulicaut, C Masson, Data Mining Query Languages, Chapter 1 in: The Data

Mining and Knowledge Discovery Handbook, O Maimon and L Rokach (Eds.), Springer,

7.4 Hỗ trợ của các DBMS ngày nay

dành cho khai phá dữ liệu

„ An initiative developed and published by the International

Organization for Standardization (ISO)

‡ Part 1: Framework

‡ Part 2: Full-Text

‡ Part 3: Spatial

‡ Part 5: Still Image

‡ Part 6: Data Mining

„ Part 6 specifies an SQL interface to data mining applications and

services through accessing data from SQL/MM-compliant relational

databases

‡ A standardized interface to data mining algorithms that can be layered

atop any objectrelational database system and even deployed as middle-ware when required

ƒ A collection of user-defined types provided for the key data mining functions, namely,

Association Rule Discovery Association Rule Discovery, Clustering, Clustering Classification Classification and Regression

Source: S S Anand, M Grobelnik, F Herrmann, D Wettschereck, M Hornick, C Lingenfelder, N.

7.4 Hỗ trợ của các DBMS ngày nay

dành cho khai phá dữ liệu

‡ The SQL Multimedia and Applications

Packages Standard (SQL/MM) – Part 6

„ User-defined types related to data

ƒ used to submit a single record of data for model application

Source: S S Anand, M Grobelnik, F Herrmann, D Wettschereck, M Hornick, C Lingenfelder,

7.4 Hỗ trợ của các DBMS ngày nay

dành cho khai phá dữ liệu

Standard (SQL/MM) – Part 6

ƒ DM_<Technique>Settings, DM_<Technique>BldTask, DM_<Technique>Model

ƒ DM_<Technique>TestTask, DM_<Technique>Model, DM_<Technique>TestResult

ƒ DM_<Technique>ApplTask, DM_<Technique>Model, DM_<Technique>Result, DM_ApplicationData

Source: S S Anand, M Grobelnik, F Herrmann, D Wettschereck, M Hornick, C Lingenfelder,

N Rooney, Knowledge discovery standards, Artif Intell Rev (2007) 27:21-56.

7.4 Hỗ trợ của các DBMS ngày nay

dành cho khai phá dữ liệu

7.4 Hỗ trợ của các DBMS ngày nay

dành cho khai phá dữ liệu

Source: S S Anand, M Grobelnik, F Herrmann, D Wettschereck, M Hornick, C Lingenfelder,

N Rooney, Knowledge discovery standards, Artif Intell Rev (2007) 27:21-56.

The application retrieves the model with the statement:

and calls the following to compute the predicted class:

7.4 Hỗ trợ của các DBMS ngày nay

dành cho khai phá dữ liệu

„ Microsoft’s OLE DB for Data Mining (OLE-DB 2000): an approach which is Microsoft

specially designed for data mining needs—it combines SQL with a low level

API (a set of COM interfaces) to achieve interoperability with other client and

server technologies.

‡ MS Nạve Bayes, MS Decision Trees, MS Time Series, MS Clustering, MS Sequence

Clustering, MS Association Rules, MS Neural Network

„ IBM’s DB2 Intelligent Miner products contain a set of DB2 database extenders IBM

(DB2-IM 2004): incorporate data mining functionality into standard database

SQL language in a relatively standard way.

‡ Functionality is based on IBM’s “Intelligent Miner” data mining product, now part of

the IBM DB2 Data Warehouse Edition V9.1.

‡ Intelligent Miner fully implements SQL/MM data mining as well as most of PMML

„ Oracle Data Mining (Oracle 2004): a set of functions available in Oracle’s Oracle

database and accessible though PL/SQL (programming language available to

database programmers) and through a Java interface.

‡ Decision Tree, Generalized Linear Models, Minimum Description Length, Nạve Bayes,

Support Vector Machines, Apriori, k-Means, Non-Negative Matrix Factorization, One Class Support Vector Machine, Orthogonal Partitioning Clustering

Source: S S Anand, M Grobelnik, F Herrmann, D Wettschereck, M Hornick, C Lingenfelder, N Rooney,