datamining - on what kind of data
TRANSCRIPT
1. RELATIONAL DATABASE
• Collection of table
• When Data Mining is applied to it, we can go further by searching for trends or
data patterns
2. DATA WAREHOUSE
• Stored to provide information for
historical perspective & typically
summarized.
• Usually modeled by
multidimensional database
structure
• The actual physical structure of a
data warehouse maybe a relational
database or a multidimensional
data cube that provide
multidimensional view of data &
allow the precomputation and fast
accessing of summarized data well
suited for OLAP
4. TRANSACTIONAL DATABASE
• File where each record represents a transaction
• Can mining such frequent patterns
4. ADVANCED DATA & INFORMATIONS SYSTEM & ADVANCED APPLICATIONS
• Widely used in bussiness application
• efficient data structures and scalable methods for handling complex object
structures; variable-length records; semistructured or unstructured data; text,
spatiotemporal, and multimedia data; and database schemas with complex
structures and dynamic changes.
4.1 OBJECT RELATIONAL DATABASE
• Providing rich data type for handling complex objects & objects orientation
• Each entity is considered as an object
• For data mining in object relational database, need developed technic for
handling complex object structures, complex data types, class & subclass
hierarchies, property inheritance and methodes & procedures
4.2 temporal database, sequence database, time-series database
Temporal database
Stores relational
data that include
time-related
attributes
Sequence database
Sequence ordered
events with or
without concrete
notion of time
Time-series database
Stores sequence of
value or events
obtained over
repeated
measurement of
time (daily, weekly)
Data mining can be used to find the characteristic of object evolution or the rends of changes for objects in the database
Can be useful for decision making and strategy planning
4.3 SPATIAL DATABASE & SPATIOTEMPORAL DATABASE
• Contain spatial relataed information, ex : maps, medical, satellite image.
• Data mining can uncovered pattern, construct models for predicting based on
the relevant set of features of the spatial objects
• A spatial database that stores spatial objects that change with time is called a
spatiotemporal database, from which interesting information can be mined.
4.4 TEXT DATABASE & MULTIMEDIA DATABASE
Text
database
Contain words description for objects
Maybe highly unstructured, semistructured or well structured
Data mining can uncover general & concise description of the text document,
clustering behaviour of text object
Standard data mining method need to be integrated with information retrieval
techniques
Multimedia
database
Support large objects
Data mining methods need to be integrated with storage and search
techniques
4.5 HETEROGENEOUS DATABASE & LEGACY DATABASE
Heterogeneous database
• Set of interconnected, autonomous
component database
Legacy database
• Group of heterogeneous database that
combine different kind of data system
Data mining can performing statistical data distribution & correlation analysis & transforming the given data into higher, more generalized, conceptual levels, from which information exchange can seen more & easily be performed
4.6 DATA STREAMS
• Where data flow in and out of an observation platform (or window) dynamically.
• Have the following unique features: huge or possibly infinite volume,
dynamically changing, flowing in and out in a fixed order, allowing only one or a
small number of scans, and demanding fast (often real-time) response time.
• Mining data streams involves the efficient discovery of general patterns and
dynamic changes within stream data.
4.7 THE WORLD WIDE WEB
• Where data objects are linked together to facilitate interactive access.
• Web mining is the development of scalable and effective web data analysis and
mining methods. It may help us learn about the distribution of information on
the web in general, characterize and classify web ages and uncover web
dynamics and the association and other relationships among different web
pages, users, communities, and web-based activities.
THANK YOU!
• DATA MINING CONCEPTS AND TECHNIQUES 2ND EDITION
JIAWEI HAN & MICHELINE KAMBER