This chapter introduces data mining and discusses its rise due to the massive growth of digital data. It describes data mining as the automated extraction of meaningful patterns from large data sets, and notes it draws on techniques from machine learning, statistics, pattern recognition, and database systems. The chapter outlines different types of data that can be mined, patterns that can be discovered, and applications of data mining in various domains including business, science, and on the web.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
2. 2
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
3. 3
Why Data Mining?
The Explosive Growth of Data: from terabytes to petabytes
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
analysis of massive data sets
4. 4
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
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!
Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science,
Comm. ACM, 45(11): 50-54, Nov. 2002
5. 5
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
6. 6
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
7. 7
What Is Data Mining?
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?
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
8. 8
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
9. 9
Example: A Web Mining Framework
Web mining usually involves
Data cleaning
Data integration from multiple sources
Warehousing the data
Data cube construction
Data selection for data mining
Data mining
Presentation of the mining results
Patterns and knowledge to be used or stored into
knowledge-base
10. 10
Data Mining in Business Intelligence
Increasing potential
to support
business decisions End User
Business
Analyst
Data
Analyst
DBA
Decision
Making
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems
11. 11
Example: Mining vs. Data Exploration
Business intelligence view
Warehouse, data cube, reporting but not much mining
Business objects vs. data mining tools
Supply chain example: tools
Data presentation
Exploration
12. 12
KDD Process: A Typical View from ML and
Statistics
Input Data Data
Mining
Data Pre-
Processing
Post-
Processing
This is a view from typical machine learning and statistics communities
Data integration
Normalization
Feature selection
Dimension reduction
Pattern discovery
Association & correlation
Classification
Clustering
Outlier analysis
… … … …
Pattern evaluation
Pattern selection
Pattern interpretation
Pattern visualization
13. 13
Example: Medical Data Mining
Health care & medical data mining – often
adopted such a view in statistics and machine
learning
Preprocessing of the data (including feature
extraction and dimension reduction)
Classification or/and clustering processes
Post-processing for presentation
14. 14
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
15. 15
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
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.
16. 16
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
17. 17
Data Mining: On What Kinds of Data?
Database-oriented data sets and applications
Relational database, data warehouse, transactional database
Advanced data sets and advanced applications
Data streams and sensor data
Time-series data, temporal data, sequence data (incl. bio-sequences)
Structure data, graphs, social networks and multi-linked data
Object-relational databases
Heterogeneous databases and legacy databases
Spatial data and spatiotemporal data
Multimedia database
Text databases
The World-Wide Web
18. 18
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
19. 19
Data Mining Function: (1) Generalization
Information integration and data warehouse construction
Data cleaning, transformation, integration, and
multidimensional data model
Data cube technology
Scalable methods for computing (i.e., materializing)
multidimensional aggregates
OLAP (online analytical processing)
Multidimensional concept description: Characterization
and discrimination
Generalize, summarize, and contrast data
characteristics, e.g., dry vs. wet region
20. 20
Data Mining Function: (2) Association and
Correlation Analysis
Frequent patterns (or frequent itemsets)
What items are frequently purchased together in your
Walmart?
Association, correlation vs. causality
A typical association rule
Diaper Beer [0.5%, 75%] (support, confidence)
Are strongly associated items also strongly correlated?
How to mine such patterns and rules efficiently in large
datasets?
How to use such patterns for classification, clustering,
and other applications?
21. 21
Data Mining Function: (3) Classification
Classification and label prediction
Construct models (functions) based on some training examples
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, …
22. 22
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
Many methods and applications
23. 23
Data Mining Function: (5) Outlier Analysis
Outlier analysis
Outlier: A data object that does not comply with the general
behavior of the data
Noise or exception? ― One person’s garbage could be another
person’s treasure
Methods: by product of clustering or regression analysis, …
Useful in fraud detection, rare events analysis
24. 24
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
Motifs and biological sequence analysis
Approximate and consecutive motifs
Similarity-based analysis
Mining data streams
Ordered, time-varying, potentially infinite, data streams
25. 25
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, …
26. 26
Evaluation of Knowledge
Are all mined knowledge interesting?
One can mine tremendous amount of “patterns” and knowledge
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
…
27. 27
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
28. 28
Data Mining: Confluence of Multiple Disciplines
Data Mining
Machine
Learning
Statistics
Applications
Algorithm
Pattern
Recognition
High-Performance
Computing
Visualization
Database
Technology
29. 29
Why Confluence of Multiple Disciplines?
Tremendous amount of data
Algorithms must be highly scalable to handle such as tera-bytes of
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 networks and multi-linked data
Heterogeneous databases and legacy databases
Spatial, spatiotemporal, multimedia, text and Web data
Software programs, scientific simulations
New and sophisticated applications
30. 30
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
31. 31
Applications of Data Mining
Web page analysis: from web page classification, clustering to
PageRank & HITS algorithms
Collaborative analysis & recommender systems
Basket data analysis to targeted marketing
Biological and medical data analysis: classification, cluster analysis
(microarray data analysis), biological sequence analysis, biological
network analysis
Data mining and software engineering (e.g., IEEE Computer, Aug.
2009 issue)
From major dedicated data mining systems/tools (e.g., SAS, MS SQL-
Server Analysis Manager, Oracle Data Mining Tools) to invisible data
mining
32. 32
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
33. 33
Major Issues in Data Mining (1)
Mining Methodology
Mining various and new kinds of knowledge
Mining knowledge in multi-dimensional space
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
Incorporation of background knowledge
Presentation and visualization of data mining results
34. 34
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
35. 35
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
36. 36
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), etc.
ACM Transactions on KDD starting in 2007
37. 37
Conferences and Journals on Data Mining
KDD Conferences
ACM SIGKDD Int. Conf. on
Knowledge Discovery in
Databases and Data Mining (KDD)
SIAM Data Mining Conf. (SDM)
(IEEE) Int. Conf. on Data Mining
(ICDM)
European Conf. on Machine
Learning and Principles and
practices of Knowledge Discovery
and Data Mining (ECML-PKDD)
Pacific-Asia Conf. on Knowledge
Discovery and Data Mining
(PAKDD)
Int. Conf. on Web Search and
Data Mining (WSDM)
Other related conferences
DB conferences: ACM SIGMOD,
VLDB, ICDE, EDBT, ICDT, …
Web and IR conferences: WWW,
SIGIR, WSDM
ML conferences: ICML, NIPS
PR conferences: CVPR,
Journals
Data Mining and Knowledge
Discovery (DAMI or DMKD)
IEEE Trans. On Knowledge and
Data Eng. (TKDE)
KDD Explorations
ACM Trans. on KDD
38. 38
Where to Find References? DBLP, CiteSeer, Google
Data mining and KDD (SIGKDD: CDROM)
Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.
Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD
Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM)
Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA
Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.
AI & Machine Learning
Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.
Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems,
IEEE-PAMI, etc.
Web and IR
Conferences: SIGIR, WWW, CIKM, etc.
Journals: WWW: Internet and Web Information Systems,
Statistics
Conferences: Joint Stat. Meeting, etc.
Journals: Annals of statistics, etc.
Visualization
Conference proceedings: CHI, ACM-SIGGraph, etc.
Journals: IEEE Trans. visualization and computer graphics, etc.
39. 39
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kind of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
What Technology Are Used?
What Kind of Applications Are Targeted?
Major Issues in Data Mining
A Brief History of Data Mining and Data Mining Society
Summary
40. 40
Summary
Data mining: Discovering interesting patterns and knowledge from
massive amount of data
A natural evolution of database 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, outlier and trend analysis, etc.
Data mining technologies and applications
Major issues in data mining
41. 41
Recommended Reference Books
S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan
Kaufmann, 2002
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000
T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and
Data Mining. AAAI/MIT Press, 1996
U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge
Discovery, Morgan Kaufmann, 2001
J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed., 2011
D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference,
and Prediction, 2nd ed., Springer-Verlag, 2009
B. Liu, Web Data Mining, Springer 2006.
T. M. Mitchell, Machine Learning, McGraw Hill, 1997
G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991
P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005
S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998
I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java
Implementations, Morgan Kaufmann, 2nd ed. 2005