PPT ON DATA MINING PRIMITIVES

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Data Mining Primitives  Presentation Transcript

1.Data Mining: Concepts and Techniques

2.Data Mining Primitives, Languages, and System Architectures
Data mining primitives: What defines a data mining task?
A data mining query language
Design graphical user interfaces based on a data mining query language
Architecture of data mining systems
Summary

3.Why Data Mining Primitives and Languages?
Finding all the patterns autonomously in a database? — unrealistic because the patterns could be too many but uninteresting
Data mining should be an interactive process
User directs what to be mined
Users must be provided with a set of primitives to be used to communicate with the data mining system
Incorporating these primitives in a data mining query language
More flexible user interaction
Foundation for design of graphical user interface
Standardization of data mining industry and practice

4.What Defines a Data Mining Task ?
Task-relevant data
Type of knowledge to be mined
Background knowledge
Pattern interestingness measurements
Visualization of discovered patterns

5.Task-Relevant Data (Minable View)
Database or data warehouse name
Database tables or data warehouse cubes
Condition for data selection
Relevant attributes or dimensions
Data grouping criteria

6.Types of knowledge to be mined
Characterization
Discrimination
Association
Classification/prediction
Clustering
Outlier analysis
Other data mining tasks

7.Background Knowledge: Concept Hierarchies

8.Measurements of Pattern Interestingness
Simplicity
    e.g., (association) rule length, (decision) tree size
Certainty
    e.g., confidence, P(A|B) = n(A and B)/ n (B), classification reliability or accuracy, certainty factor, rule strength, rule quality, discriminating weight, etc.
Utility
    potential usefulness, e.g., support (association), noise threshold (description)
Novelty
    not previously known, surprising (used to remove redundant rules, e.g., Canada vs. Vancouver rule implication support ratio

9.Visualization of Discovered Patterns
Different backgrounds/usages may require different forms of representation
E.g.,  rules, tables, crosstabs, pie/bar chart etc.
Concept hierarchy is also important
Discovered knowledge might be more understandable when represented at high level of abstraction
Interactive drill up/down, pivoting, slicing and dicing provide different perspective to data
Different kinds of knowledge require different representation: association, classification, clustering, etc.

10.Data Mining Primitives, Languages, and System Architectures
Data mining primitives: What defines a data mining task?
A data mining query language
Design graphical user interfaces based on a data mining query language
Architecture of data mining systems
Summary

11.A Data Mining Query Language (DMQL)
Motivation
A DMQL can provide the ability to support ad-hoc and interactive data mining
By providing a standardized language like SQL
Hope to achieve a similar effect like that SQL has on relational database
Foundation for system development and evolution
Facilitate information exchange, technology transfer, commercialization and wide acceptance
Design
DMQL is designed with the primitives described earlier

12.Syntax for DMQL
Syntax for specification of
task-relevant data
the kind of knowledge to be mined
concept hierarchy specification
interestingness measure
pattern presentation and visualization
Putting it all together — a DMQL query

13.Syntax for task-relevant data specification
use database database_name, or use data warehouse data_warehouse_name
from relation(s)/cube(s) [where condition]
in relevance to att_or_dim_list
order by order_list
group by grouping_list
having condition

14.Specification of task-relevant data

15.Syntax for specifying the kind of knowledge to be mined
Characterization
    Mine_Knowledge_Specification  ::=     mine characteristics [as pattern_name]     analyze measure(s)
Discrimination
    Mine_Knowledge_Specification  ::=     mine comparison [as pattern_name]     for target_class where target_condition      {versus contrast_class_i where contrast_condition_i}      analyze measure(s)
Association
    Mine_Knowledge_Specification  ::=     mine associations [as pattern_name]

16.Classification
    Mine_Knowledge_Specification  ::=     mine classification [as pattern_name]     analyze classifying_attribute_or_dimension
Prediction
Mine_Knowledge_Specification  ::= mine prediction [as pattern_name] analyze prediction_attribute_or_dimension {set {attribute_or_dimension_i= value_i}}

17.Syntax for concept hierarchy specification

18.Syntax for interestingness measure specification
Interestingness measures and thresholds can be specified by the user with the statement:
with <interest_measure_name>  threshold = threshold_value
Example:
with support threshold = 0.05
with confidence threshold = 0.7 

19.Syntax for pattern presentation and visualization specification
We have syntax which allows users to specify the display of discovered patterns in one or more forms    
            display as <result_form>
 To facilitate interactive viewing at different concept level, the following syntax is defined:

Multilevel_Manipulation  ::=   roll up on attribute_or_dimension                 | drill down on attribute_or_dimension                 | add attribute_or_dimension                     | drop attribute_or_dimension

20.Putting it all together: the full specification of a DMQL query
use database AllElectronics_db
use hierarchy location_hierarchy  for B.address
mine characteristics as  customerPurchasing
analyze  count%
in relevance to C.age, I.type, I.place_made
from  customer C,  item I, purchases P, items_sold S, works_at W, branch
where I.item_ID = S.item_ID  and S.trans_ID = P.trans_ID
    and P.cust_ID = C.cust_ID and P.method_paid = ``AmEx''  
    and P.empl_ID = W.empl_ID and W.branch_ID = B.branch_ID and B.address = ``Canada"  and I.price >= 100
with noise threshold = 0.05
display as table

21.Other Data Mining Languages & Standardization Efforts
Association rule language specifications
MSQL (Imielinski & Virmani’99)
MineRule (Meo Psaila and Ceri’96)
Query flocks based on Datalog syntax (Tsur et al’98)
OLEDB for DM (Microsoft’2000)
Based on OLE, OLE DB, OLE DB for OLAP
Integrating DBMS, data warehouse and data mining
CRISP-DM (CRoss-Industry Standard Process for Data Mining)
Providing a platform and process structure for effective data mining
Emphasizing on deploying data mining technology to solve business problems

22.Data Mining Primitives, Languages, and System Architectures
Data mining primitives: What defines a data mining task?
A data mining query language
Design graphical user interfaces based on a data mining query language
Architecture of data mining systems
Summary

23.Designing Graphical User Interfaces based on a data mining query language
What tasks should be considered in the design GUIs based on a data mining query language?
Data collection and data mining query composition
Presentation of discovered patterns
Hierarchy specification and manipulation
Manipulation of data mining primitives
Interactive multilevel mining
Other miscellaneous information

24.Data Mining Primitives, Languages, and System Architectures
Data mining primitives: What defines a data mining task?
A data mining query language
Design graphical user interfaces based on a data mining query language
Architecture of data mining systems
Summary

25.Data Mining System Architectures
Coupling data mining system with DB/DW system
No coupling—flat file processing, not recommended
Loose coupling
Fetching data from DB/DW
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 stat functions
Tight coupling—A uniform information processing environment
DM is smoothly integrated into a DB/DW system, mining query is optimized based on mining query, indexing, query processing methods, etc.

26.Data Mining Primitives, Languages, and System Architectures
Data mining primitives: What defines a data mining task?
A data mining query language
Design graphical user interfaces based on a data mining query language
Architecture of data mining systems
Summary

27.Summary
Five primitives for specification of a data mining task
task-relevant data
kind of knowledge to be mined
background knowledge
interestingness measures
knowledge presentation and visualization techniques to be used for displaying the discovered patterns
Data mining query languages
DMQL, MS/OLEDB for DM, etc.
Data mining system architecture
No coupling, loose coupling, semi-tight coupling, tight coupling