Chapter 4: Data Mining Primitives, Languages, and System Architectures

1. Chapter 4: 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 Data Mining: Concepts and Techniques

2. Unit II • Data Mining Primitive, Languages, and System Architecture : • Data mining primitive, • Data Mining Query Languages, • Designing Graphical User Interfaces Based on a Data Mining Query Language • Architecture of Data Mining Systems Data Mining: Concepts and Techniques

3. Data Mining Primitives, Languages, and System Architecture • Misconception: Data mining systems can autonomously dig out all of the valuable knowledge from a given large database, without human intervention. • If there was no user intervention then the system would uncover a large set of patterns that may even surpass the size of the database. Hence, user interference is required. • This user communication with the system is provided by using a set of data mining primitives. Data Mining: Concepts and Techniques

4. Why Data Mining Primitives and Languages? • A popular misconception about data mining is to expect that data mining systems can autonomously dig out all of the valuable knowledge and patterns that is embedded in large database, without human intervention or guidance. • 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 primitivesto 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 Data Mining: Concepts and Techniques

5. Data Mining Primitives :What Defines a Data Mining Task ? • Task-relevant data : What is the data set I want to mine? • Type of knowledge to be mined : What kind of knowledge do I want to mine ? • Background knowledge : What background knowledge could be useful here ? • Pattern interestingness measurements : What measures can be useful to estimate pattern interestingness ? • Visualization of discovered patterns : How do I want the discovered patterns to be presented ? Data Mining: Concepts and Techniques

6. Primitives for specifying a data mining task Data Mining: Concepts and Techniques

7. Task-Relevant Data (Minable View) • The first primitive is the specification of the data on which mining is to be performed. • Typically, a user is interested in only a subset of the database. It is impractical to mine the entire database, particularly since the number of patterns generated could be exponential w.r.t the database size. • Furthermore, many of the patterns found would be irrelevant to the interests of the user. • In a relational database, the set of task relevant data can be collected via a relational query involving operations like selection, projection, join and aggregation. • This retrieval of data can be thought of as a “subtask” of the data mining task. The data collection process results in a new data relational called the initial data relation Data Mining: Concepts and Techniques

8. The initial data relation can be ordered or grouped according to the conditions specified in the query. • The data may be cleaned or transformed (e.g. aggregated on certain attributes) prior to applying data mining analysis. • This initial relation may or may not correspond to physical relation in the database. • Since virtual relations are called Views in the field of databases, the set of task-relevant data for data mining is called a minable view • If data mining task is to study associations between items frequently purchased at AllElectronics by customers in Canada, the task relevant data can be specified by providing the following information Data Mining: Concepts and Techniques

9. 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 Data Mining: Concepts and Techniques

10. Task relevant data • Data portion to be investigated. • Attributes of interest (relevant attributes) can be specified. • Initial data relation • Minable view Data Mining: Concepts and Techniques

11. Example If a data mining task is to study associations between items frequently purchased at All Electronics by customers in Canada, the task relevant data can be specified by providing the following information: • Name of the database or data warehouse to be used (e.g., AllElectronics_db) • Names of the tables or data cubes containing relevant data (e.g., item, customer, purchases and items_sold) • Conditions for selecting the relevant data (e.g., retrieve data pertaining to purchases made in Canada for the current year) • The relevant attributes or dimensions (e.g., name and price from the item table and income and age from the customer table) Data Mining: Concepts and Techniques

12. The kind of knowledge to be mined It is important to specify the kind of knowledge to be mined, as this determines the data mining functions to be performed. The kinds of knowledge include concept description (characterization and discrimination), association, classification, predication, clustering, and evolution analysis. In addition to specifying the kind of knowledge to be mined for a given data mining task, the user can be more specific and provide pattern templates that all discovered patterns must match Data Mining: Concepts and Techniques

13. The kind of knowledge to be mined These templates, or metapatterns (also called metarules or metaqueries), can be used to guide the discovery process. The use of metapatterns is illustrated in the following example. A user studying the buying habits of Allelectronics customers may choose to mine association rules of the form: P (X:customer,W) ^ Q (X,Y) => buys (X,Z) Here X is a key of the customer relations, P & Q are predicate variables and W,Y and Z are object variables [1.4%, 70%] Data Mining: Concepts and Techniques

14. The kind of knowledge to be mined The search for association rules is confined to those matching the given metarule, such as age (X, “30…..39”) ^ income (X, “40k….49K”) => buys (X, “VCR”)[2.2%, 60%] and occupation (X, “student ”) ^ age (X, “20…..29”)=> buys (X, “computer”)[1.4%, 70%] The former rule states that customers in their thirties, with an annual income of between 40K and 49K, are likely (with 60% confidence) to purchase a VCR, and such cases represent about 2.2.% of the total number of transactions. The latter rule states that customers who are students and in their twenties are likely (with 70% confidence) to purchase a computer, and such cases represent about 1.4% of the total number of transactions. Data Mining: Concepts and Techniques

15. Types of knowledge to be mined • Characterization • Discrimination • Association • Classification/prediction • Clustering • Outlier analysis • Other data mining tasks Data Mining: Concepts and Techniques

16. The data mining functionalities and the variety of knowledge they discover are briefly presented in the following list: Characterization: Data characterization is a summarization of general features of objects in a target class, and produces what is called characteristic rules. The data relevant to a user-specified class are normally retrieved by a database query and run through a summarization module to extract the essence of the data at different levels of abstractions. For example, one may want to characterize the OurVideoStore customers who regularly rent more than 30 movies a year. With concept hierarchies on the attributes describing the target class, the attribute-oriented induction method can be used, for example, to carry out data summarization. Note that with a data cube containing summarization of data, simple OLAP operations fit the purpose of data characterization. Data Mining: Concepts and Techniques

17. Discrimination: Data discrimination produces what are called discriminant rules and is basically the comparison of the general features of objects between two classes referred to as the target class and the contrasting class. For example, one may want to compare the general characteristics of the customers who rented more than 30 movies in the last year with those whose rental account is lower than 5. The techniques used for data discrimination are very similar to the techniques used for data characterization with the exception that data discrimination results include comparative measures. Data Mining: Concepts and Techniques

18. Association analysis: Association analysis is the discovery of what are commonly called association rules. It studies the frequency of items occurring together in transactional databases, and based on a threshold called support, identifies the frequent item sets. Another threshold, confidence, which is the conditional probability than an item appears in a transaction when another item appears, is used to pinpoint association rules. Association analysis is commonly used for market basket analysis. For example, it could be useful for the OurVideoStore manager to know what movies are often rented together or if there is a relationship between renting a certain type of movies and buying popcorn or pop. The discovered association rules are of the form: P -> Q [s,c], where P and Q are conjunctions of attribute value-pairs, and s (for support) is the probability that P and Q appear together in a transaction and c (for confidence) is the conditional probability that Q appears in a transaction when P is present. Data Mining: Concepts and Techniques

19. For example, the hypothetic association rule: RentType(X, "game") AND Age(X, "13-19") -> Buys(X, "pop") [s=2% ,c=55%]would indicate that 2% of the transactions considered are of customers aged between 13 and 19 who are renting a game and buying a pop, and that there is a certainty of 55% that teenage customers who rent a game also buy pop. Data Mining: Concepts and Techniques

20. Classification: Classification analysis is the organization of data in given classes. Also known as supervised classification, the classification uses given class labels to order the objects in the data collection. Classification approaches normally use a training set where all objects are already associated with known class labels. The classification algorithm learns from the training set and builds a model. The model is used to classify new objects. For example, after starting a credit policy, the OurVideoStore managers could analyze the customers’ behaviours vis-à-vis their credit, and label accordingly the customers who received credits with three possible labels "safe", "risky" and "very risky". The classification analysis would generate a model that could be used to either accept or reject credit requests in the future. Data Mining: Concepts and Techniques

21. Prediction: Prediction has attracted considerable attention given the potential implications of successful forecasting in a business context. There are two major types of predictions: one can either try to predict some unavailable data values or pending trends, or predict a class label for some data. The latter is tied to classification. Once a classification model is built based on a training set, the class label of an object can be foreseen based on the attribute values of the object and the attribute values of the classes. Prediction is however more often referred to the forecast of missing numerical values, or increase/ decrease trends in time related data. The major idea is to use a large number of past values to consider probable future values. Data Mining: Concepts and Techniques

22. Clustering: Similar to classification, clustering is the organization of data in classes. However, unlike classification, in clustering, class labels are unknown and it is up to the clustering algorithm to discover acceptable classes. Clustering is also called unsupervised classification, because the classification is not dictated by given class labels. There are many clustering approaches all based on the principle of maximizing the similarity between objects in a same class (intra-class similarity) and minimizing the similarity between objects of different classes (inter-class similarity). Data Mining: Concepts and Techniques

23. Outlier analysis: Outliers are data elements that cannot be grouped in a given class or cluster. Also known as exceptions or surprises, they are often very important to identify. While outliers can be considered noise and discarded in some applications, they can reveal important knowledge in other domains, and thus can be very significant and their analysis valuable. Data Mining: Concepts and Techniques

24. Evolution and deviation analysis: Evolution and deviation analysis pertain to the study of time related data that changes in time. Evolution analysis models evolutionary trends in data, which consent to characterizing, comparing, classifying or clustering of time related data. Deviation analysis, on the other hand, considers differences between measured values and expected values, and attempts to find the cause of the deviations from the anticipated values. Data Mining: Concepts and Techniques

25. Background Knowledge: Concept Hierarchies • It is the information about the domain to be mined • Concept hierarchy: is a powerful form of background knowledge. It allows the discovery of knowledge at multiple level of abstraction. • Concept hierarchy defines a sequence of mappings from a set of low – level concepts to higher – level, more general concepts. A concept hierarchy for the dimension location is shown in figure, mapping low-level concepts (i.e. cities) to more general concepts (i.e. countries) • Concept hierarchy consists of four levels. In our example, level 1 represents the concept country, while levels 2 and 3 represents the concepts province_or_state and city resp Data Mining: Concepts and Techniques

26. all Level 0 USA Level 1 Canada British Columbia Ontario New York Illinois Level 2 Level 3 Vancouver Victoria Toronto Ottawa New York Buffalo Chicago Example Data Mining: Concepts and Techniques

27. Four major types of concept hierarchies: Schema hierarchies Set-grouping hierarchies Operation-derived hierarchies Rule-based hierarchies Data Mining: Concepts and Techniques

28. 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 login-name < department < university < country • Rule-based hierarchy • low_profit_margin (X) <= price(X, P1) and cost (X, P2) and (P1 - P2) < $50 Data Mining: Concepts and Techniques

29. Concept hierarchies (2) • Rolling Up - Generalization of data Allows to view data at more meaningful and explicit abstractions. Makes it easier to understand Compresses the data Would require fewer input/output operations • Drilling Down - Specialization of data • Concept values replaced by lower level concepts • There may be more than concept hierarchy for a given attribute or dimension based on different user viewpoints • Example: Regional sales manager may prefer the previous concept hierarchy but marketing manager might prefer to see location with respect to linguistic lines in order to facilitate the distribution of commercial ads. Data Mining: Concepts and Techniques

30. Schema hierarchies • Schema hierarchy is the total or partial order among attributes in the database schema. • Schema hierarchy may formally express existing semantic relationships between attributes. • Typically a schema hierarchy specifies a data warehouse dimension • Example: location hierarchy street < city < province/state < country • This means that street is at conceptually lower level than city, which is lower than province_or_state, which is conceptually lower than country. • A schema hierarchy provides metadata information. DWM 3.1 30 Data Mining: Concepts and Techniques

31. Set-grouping hierarchies • Organizes values for a given attribute into groups or sets or range of values. • Total or partial order can be defined among groups. • Used to refine or enrich schema-defined hierarchies. • Typically used for small sets of object relationships. • Example: Set-grouping hierarchy for age {young, middle_aged, senior} all (age) {20….29} young {40….59} middle_aged {60….89} senior Data Mining: Concepts and Techniques

32. Operation-derived hierarchies • Operation-derived: An operation derived hierarchy is based on operations specified by users, experts, or the data mining system. Operations may include decoding of information-encoded strings, information extraction from complex data objects, and data clustering Example: URL or email address xyz@cs.iitm.in gives login name < dept. < univ. < country Data Mining: Concepts and Techniques

33. Rule-based hierarchies • Rule-based: Occurs when either whole or portion of a concept hierarchy is defined as a set of rules and is evaluated dynamically based on current database data and rule definition • Example: Following rules are used to categorize items as low_profit, medium_profit and high_profit_margin. low_profit_margin(X) <= price(X,P1)^cost(X,P2)^((P1-P2)<50) medium_profit_margin(X) <= price(X,P1)^cost(X,P2)^((P1-P2)≥50)^((P1-P2)≤250) high_profit_margin(X) <= price(X,P1)^cost(X,P2)^((P1-P2)>250) DWM 3.1 33 Data Mining: Concepts and Techniques

34. Interestingness measure (1) • Although specification of the task relevant data and of the kind of knowledge to be mined (e.g. characterization, association, etc.) may substantially reduce the number of pattern generated, a data mining process may still generate a large number of patterns • Typically, only a small fraction of these patterns will actually be of interest to the given user. Thus, users need to further confine the number of uninteresting patterns returned by the process. This can be achieved by specifying interestingness measures that estimate the simplicity, certainty, utility, and novelty of patterns • We will see some objective measures of pattern interestingness. In general, each measure is associated with a threshold that can be controlled by the user. • Rules that do not meet the threshold are considered uninteresting, and hence are not presented to the user as knowledge Data Mining: Concepts and Techniques

35. Interestingness measure (1) Simplicity: A factor contributing to the interestingness of a pattern is the pattern’s overall simplicity for human comprehension. Objective measures of pattern simplicity can be viewed as functions of the pattern structure, defined in terms of the pattern size in bits, or the number of attributes or operators appearing in the pattern. For example, the more complex the structure of a rule is, the more difficult it is to interpret, and hence, the less interesting it is likely to be Rule Length : It is a simplicity measure Data Mining: Concepts and Techniques

36. Interestingness measure (1) Certainty (Confidence) : Each discovered pattern should have a measure of certainty associated with it that assesses the validity or “trustworthiness” of the pattern. A certainty measure for association rules of the form “A =>B” where A and B are sets of items, is confidence. Confidence is a certainty measure. Given a set of task-relevant data tuples the confidence of “A => B” is defined as confidence (A=>B) = # tuples containing both A and B# tuples containing A A confidence of 85% for the rulebuys(X, “computer”) => buys (X,“software”) means that 85% of all customers who purchased a computer also bought software Data Mining: Concepts and Techniques

37. Interestingness measure (1) Utility (Support) : The potential usefulness of a pattern is a factor defining its interestingness. It can be estimated by a utility function, such as support. The support of an association pattern refers to the percentage of task relevant data tuples (or transactions) for which the pattern is true. • Utility (support) : usefulness of a pattern support (A=>B) = # tuples containing both A and Btotal # of tuples A support of 30% for the above rule means that 30% of all customers in the computer department purchased both a computer and software. • Association rules that satisfy both the minimum confidence and support threshold are referred to as strong association rules. Data Mining: Concepts and Techniques

38. Interestingness measure (1) Novelty : Novel patterns are those that contribute new information or increased performance to the given pattern set. For ex. A data exception. Another strategy for detecting novelty is to remove redundant patterns. Data Mining: Concepts and Techniques

39. Presentation and visualization • For data mining to be effective, data mining systems should be able to display the discovered patterns in multiple forms, such as rules, tables, cross tabs (cross-tabulations), pie or bar charts, decision trees, cubes, or other visual representations. • User must be able to specify the forms of presentation to be used for displaying the discovered patterns. Data Mining: Concepts and Techniques

40. DMQL • Adopts SQL-like syntax • Hence, can be easily integrated with relational query languages • Defined in BNF grammar • [ ] represents 0 or one occurrence • { } represents 0 or more occurrences • Words in sans serif represent keywords Data Mining: Concepts and Techniques

41. 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 Data Mining: Concepts and Techniques

42. 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 Data Mining: Concepts and Techniques

43. DMQL-Syntax for task-relevant data specification • Names of the relevant database or data warehouse, conditions and relevant attributes or dimensions must be specified • use database ‹database_name› or use data warehouse ‹data_warehouse_name› • from ‹relation(s)/cube(s)› [where condition] • in relevance to ‹attribute_or_dimension_list› • order by ‹order_list› • group by ‹grouping_list› • having ‹condition› Data Mining: Concepts and Techniques

44. Example Data Mining: Concepts and Techniques

45. Syntax for Kind of Knowledge to be Mined • Characterization : ‹Mine_Knowledge_Specification›  ::= mine characteristics [as ‹pattern_name›] analyze ‹measure(s)› • Example: mine characteristics as customerPurchasing analyze count% • 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)› • Example: Mine comparison as purchaseGroups for bigspenders where avg(I.price) >= $100 versus budgetspenders where avg(I.price) < $100 analyze count Data Mining: Concepts and Techniques

46. Syntax for Kind of Knowledge to be Mined (2) • Association: ‹Mine_Knowledge_Specification›   ::= mine associations [as ‹pattern_name›] [matching ‹metapattern›] • Example: mine associations as buyingHabits matching P(X: customer, W) ^ Q(X,Y) => buys (X,Z) • Classification: ‹Mine_Knowledge_Specification›   ::= mine classification [as ‹pattern_name›] analyze ‹classifying_attribute_or_dimension› • Example: mine classification as classifyCustomerCreditRating analyze credit_rating Data Mining: Concepts and Techniques

47. Syntax for concept hierarchy specification • More than one concept per attribute can be specified • Use hierarchy ‹hierarchy_name› for ‹attribute_or_dimension› • Examples: Schemaconcept hierarchy (ordering is important) • define hierarchy location_hierarchy on address as [street,city,province_or_state,country] Set-Grouping concept hierarchy • 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 Data Mining: Concepts and Techniques

48. Syntax for concept hierarchy specification (2) operation-derived concept hierarchy • define hierarchy age_hierarchyfor age on customer as {age_category(1), ..., age_category(5)} := cluster (default, age, 5) < all(age) rule-based concept hierarchy • define hierarchy profit_margin_hierarchy on itemas 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 Data Mining: Concepts and Techniques

49. Syntax for interestingness measure specification • with [‹interest_measure_name›] threshold = ‹threshold_value› • Example: • with support threshold= 5% • with confidence threshold= 70% Data Mining: Concepts and Techniques

50. Syntax for pattern presentation and visualization specification • display as ‹result_form› • The result form can be rules, tables, cubes, crosstabs, pie or bar charts, decision trees, curves or surfaces. • To facilitate interactive viewing at different concept levels or different angles, 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› Data Mining: Concepts and Techniques