1 UNIT-3 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
2 Data mining primitives: What defines a data mining task?
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 Lecture-18 - Data mining primitives: What defines a data mining task?
6 Types of knowledge to be minedCharacterization Discrimination Association Classification/prediction Clustering Outlier analysis Other data mining tasks Lecture-18 - Data mining primitives: What defines a data mining task?
7 Background Knowledge: Concept HierarchiesSchema hierarchy street < city < province_or_state < country Set-grouping hierarchy {20-39} = young, {40-59} = middle_aged Operation-derived hierarchy address: login-name < 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 InterestingnessSimplicity association rule length, decision tree size Certainty confidence, P(A|B) = n(A and B)/ n (B), classification reliability or accuracy, certainty factor, rule strength, rule quality, discriminating weight Utility potential usefulness, support (association), noise threshold (description) Novelty not previously known, surprising (used to remove redundant rules, Canada vs. Vancouver rule implication support ratio
9 Visualization of Discovered PatternsDifferent backgrounds/usages may require different forms of representation rules, tables, cross tabs, pie/bar chart 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
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 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 Lecture A data mining query language
12 Syntax for DMQL Syntax for specification of task-relevant datathe kind of knowledge to be mined concept hierarchy specification interestingness measure pattern presentation and visualization — a DMQL query Lecture A data mining query language
13 Syntax for task-relevant data specificationuse 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 Syntax for specifying the kind of knowledge to be minedCharacterization 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] Lecture A data mining query language
15 Syntax for specifying the kind of knowledge to be minedClassification 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}} Lecture A data mining query language
16 Syntax for concept hierarchy specificationTo specify what concept hierarchies to use use hierarchy
17 Syntax for concept hierarchy specificationoperation-derived hierarchies define hierarchy age_hierarchy for age on customer as {age_category(1), ..., age_category(5)} := cluster(default, age, 5) < all(age)
18 Syntax for concept hierarchy specificationrule-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 Syntax for interestingness measure specificationInterestingness measures and thresholds can be specified by the user with the statement: with
20 Syntax for pattern presentation and visualization specificationsyntax which allows users to specify the display of discovered patterns in one or more forms display as
21 The full specification of a DMQL queryuse 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 EffortsAssociation 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 languageWhat 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 ArchitecturesCoupling data mining system with DB/DW system No coupling—flat file processing, 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- sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of some stat functions
27 Data Mining System ArchitecturesTight 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