DATA MINING PRIMITIVES, LANGUAGES, AND SYSTEM ARCHITECTURES

Topics
 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
 Schema hierarchy
 E.g., street < city < province_or_state < country  Set-grouping hierarchy  E.g., 20-39 = young, 40-59 = middle_aged  Operation-derived hierarchy  email address: dmbook@cs.sfu.ca loginname < department < university < country  Rule-based hierarchy  low_profit_margin (X) <= price(X, P1) and cost (X, P2) and (P1 – P2) < $50 8 Measurements of Pattern Interestingness  Simplicity e.g., (association) rule length, (decision) tree size  Certainty e.g., confidence, P(A|B) = #(A and B)/ #(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 perspectives to data  Different kinds of knowledge require different representation: association, classification, clustering, etc. 10 A DATA MINING QUERY LANGUAGE 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: Specification of Task-Relevant Data  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: 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) E.g. mine comparison as purchaseGroups for bigSpenders where avg(I.price) >= $100
versus budgetSpenders where avg(I.price) < $100 analyze count 16 Syntax: Kind of Knowledge to Be Mined (cont.)  Association Mine_Knowledge_Specification ::= mine associations [as pattern_name] [matching ]
E.g. mine associations as buyingHabits
matching P(X:custom, W) ^ Q(X, Y)=>buys(X, Z)
 Classification
Mine_Knowledge_Specification ::=
mine classification [as pattern_name]
analyze classifying_attribute_or_dimension
 Other Patterns
clustering, outlier analysis, prediction …
17
Syntax: Concept Hierarchy Specification
 To specify what concept hierarchies to use
use hierarchy for
 We use different syntax to define different type of hierarchies
 schema hierarchies
define hierarchy time_hierarchy on date as [date,month
quarter,year]
 set-grouping hierarchies
define hierarchy age_hierarchy for age on customer as
level1: young, middle_aged, senior < level0: all level2: 20, …, 39 < level1: young level2: 40, …, 59 < level1: middle_aged level2: 60, …, 89 < level1: senior 18 Concept Hierarchy Specification (Cont.)  operation-derived hierarchies define hierarchy age_hierarchy for age on customer as age_category(1), …, age_category(5) := cluster(default, age, 5) < all(age)  rule-based hierarchies define hierarchy profit_margin_hierarchy on item as level_1: low_profit_margin < level_0: all if (price – cost)< $50 level_1: medium-profit_margin < level_0: all if ((price – cost) > $50) and ((price – cost) <= $250)) level_1: high_profit_margin < level_0: all if (price – cost) > $250
19
Specification of Interestingness Measures
 Interestingness measures and thresholds can be
specified by a user with the statement:
with threshold =
threshold_value
 Example:
with support threshold = 0.05
with confidence threshold = 0.7
20
Specification of Pattern Presentation
 Specify the display of discovered patterns
display as
 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
21
Putting it all together: 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
22
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
23
DESIGN GRAPHICAL USER
INTERFACES BASED ON A DATA
MINING QUERY LANGUAGE
24
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
25
ARCHITECTURE OF DATA MINING
SYSTEMS
26
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.
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
28
Thank you !!!
Questions:
29

1. Explain Data Mining primitives and what defines a data
   mining task.
2. Explain concept hierarchies.
3. Explain about task relevant data.
4. Discuss about presentation and visualization of
   discovered patterns by giving an example.

The post DATA MINING PRIMITIVES, LANGUAGES, AND SYSTEM ARCHITECTURES appeared first on My Assignment Online.

Expert Help With This Assignment — On Your Terms