Data Mining Primitives, Languages and System Architecture: Data Mining Primitives, Languages and System Architecture CSE 634-Datamining Concepts and Techniques Professor Anita Wasilewska Presented By Sushma Devendrappa - 105526184 Swathi Kothapalli - 105531380


Sources/References: Sources/References Data Mining Concepts and Techniques –Jiawei Han and Micheline Kamber, 2003 Handbook of Data Mining and Discovery- Willi Klosgen and Jan M Zytkow, 2002 Lydia: A System for Large-Scale News Analysis- String Processing and Information Retrieval: 12th International Conference, SPRING 2005, Buenos Aires, Argentina, November 2-4 2005. Information Retrieval: Data Structures and Algorithms - W. Frakes and R. Baeza-Yates, 1992 Geographical Information System - http://erg.usgs.gov/isb/pubs/gis_poster/


Content : Content Data mining primitives Languages System architecture Application – Geographical information system (GIS) Paper - Lydia: A System for Large-Scale News Analysis


Introduction: Introduction Motivation- need to extract useful information and knowledge from a large amount of data (data explosion problem) Data Mining tools perform data analysis and may uncover important data patterns, contributing greatly to business strategies, knowledge bases, and scientific and medical research.


What is Data Mining???: What is Data Mining??? Data mining refers to extracting or “mining” knowledge from large amounts of data. Also referred as Knowledge Discovery in Databases. It is a process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories.


Architecture of a typical data mining system: Architecture of a typical data mining system


Slide7: 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 Primitives: Data Mining Primitives Data mining primitives define a data mining task, which can be specified in the form of a data mining query. Task Relevant Data Kinds of knowledge to be mined Background knowledge Interestingness measure Presentation and visualization of discovered patterns


Task relevant data: Task relevant data Data portion to be investigated. Attributes of interest (relevant attributes) can be specified. Initial data relation Minable view


Example: Example If a 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: 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)


Kind of knowledge to be mined : Kind of knowledge to be mined It is important to specify the knowledge to be mined, as this determines the data mining function to be performed. Kinds of knowledge include concept description, association, classification, prediction and clustering. User can also provide pattern templates. Also called metapatterns or metarules or metaqueries.


Example: 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) Meta rules such as the following can be specified: age (X, “30…..39”) ^ income (X, “40k….49K”) => buys (X, “VCR”) [2.2%, 60%] occupation (X, “student ”) ^ age (X, “20…..29”)=> buys (X, “computer”) [1.4%, 70%]


Background knowledge: Background knowledge It is the information about the domain to be mined Concept hierarchy: is a powerful form of background knowledge. Four major types of concept hierarchies: schema hierarchies set-grouping hierarchies operation-derived hierarchies rule-based hierarchies


Concept hierarchies (1): Concept hierarchies (1) Defines a sequence of mappings from a set of low-level concepts to higher-level (more general) concepts. Allows data to be mined at multiple levels of abstraction. These allow users to view data from different perspectives, allowing further insight into the relationships. Example (location)


Example: Example


Concept hierarchies (2): 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.


Schema hierarchies: Schema hierarchies Schema hierarchy is the total or partial order among attributes in the database schema. May formally express existing semantic relationships between attributes. Provides metadata information. Example: location hierarchy street < city < province/state < country


Set-grouping hierarchies: 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


Operation-derived hierarchies: Operation-derived hierarchies Operation-derived: based on operations specified operations may include decoding of information-encoded strings information extraction from complex data objects data clustering Example: URL or email address xyz@cs.iitm.in gives login name < dept. < univ. < country


Rule-based hierarchies: 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) 250)


Interestingness measure (1): Interestingness measure (1) Used to confine the number of uninteresting patterns returned by the process. Based on the structure of patterns and statistics underlying them. Associate a threshold which can be controlled by the user. patterns not meeting the threshold are not presented to the user. Objective measures of pattern interestingness: simplicity certainty (confidence) utility (support) novelty


Interestingness measure (2): Interestingness measure (2) Simplicity a patterns interestingness is based on its overall simplicity for human comprehension. Example: Rule length is a simplicity measure Certainty (confidence) Assesses the validity or trustworthiness of a pattern. confidence is a certainty measure confidence (A=>B) = # tuples containing both A and B # tuples containing A A confidence of 85% for the rule buys(X, “computer”)=>buys(X,“software”) means that 85% of all customers who purchased a computer also bought software


Interestingness measure (3): Interestingness measure (3) Utility (support) usefulness of a pattern support (A=>B) = # tuples containing both A and B total # of tuples A support of 30% for the previous 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. Novelty Patterns contributing new information to the given pattern set are called novel patterns (example: Data exception). removing redundant patterns is a strategy for detecting novelty.


Presentation and visualization: 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, crosstabs (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 query languages: Data mining query languages Data mining language must be designed to facilitate flexible and effective knowledge discovery. Having a query language for data mining may help standardize the development of platforms for data mining systems. But designed a language is challenging because data mining covers a wide spectrum of tasks and each task has different requirement. Hence, the design of a language requires deep understanding of the limitations and underlying mechanism of the various kinds of tasks.


Data mining query languages (2): Data mining query languages (2) So…how would you design an efficient query language??? Based on the primitives discussed earlier. DMQL allows mining of different kinds of knowledge from relational databases and data warehouses at multiple levels of abstraction.


DMQL: 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


DMQL-Syntax for task-relevant data specification: 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›


Example : Example


Syntax for Kind of Knowledge to be Mined: 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


Syntax for Kind of Knowledge to be Mined (2): 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


Syntax for concept hierarchy specification: Syntax for concept hierarchy specification More than one concept per attribute can be specified Use hierarchy ‹hierarchy_name› for ‹attribute_or_dimension› Examples: Schema concept 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


Syntax for concept hierarchy specification (2): Syntax for concept hierarchy specification (2) operation-derived concept hierarchy define hierarchy age_hierarchy for age on customer as {age_category(1), ..., age_category(5)} := cluster (default, age, 5) $50) and ((price - cost) $250


Syntax for interestingness measure specification: Syntax for interestingness measure specification with [‹interest_measure_name›] threshold = ‹threshold_value› Example: with support threshold = 5% with confidence threshold = 70%


Syntax for pattern presentation and visualization specification: 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›


Architectures of Data Mining System: Architectures of Data Mining System With popular and diverse application of data mining, it is expected that a good variety of data mining system will be designed and developed. Comprehensive information processing and data analysis will be continuously and systematically surrounded by data warehouse and databases. A critical question in design is whether we should integrate data mining systems with database systems. This gives rise to four architecture: - No coupling - Loose Coupling - Semi-tight Coupling - Tight Coupling


Cont. : Cont. No Coupling: DM system will not utilize any functionality of a DB or DW system Loose Coupling: DM system will use some facilities of DB and DW system like storing the data in either of DB or DW systems and using these systems for data retrieval Semi-tight Coupling: Besides linking a DM system to a DB/DW systems, efficient implementation of a few DM primitives. Tight Coupling: DM system is smoothly integrated with DB/DW systems. Each of these DM, DB/DW is treated as main functional component of information retrieval system.


Paper Discussion: Paper Discussion Lydia: A System for Large-Scale News Analysis Levon Lloyd, Dimitrios Kechagias, Steven Skiena Department of Computer Science State University of New York at Stony Brook Published in 12th International Conference SPRING 2005, Buenos Aires, Argentina, November 2-4 2005


Abstract: Abstract This paper is on “Text Mining” system called Lydia. Periodical publications represent a rich and recurrent source of knowledge on both current and historical events. The Lydia project seeks to build a relational model of people, places, and things through natural language processing of news sources and the statistical analysis of entity frequencies and co-locations. Perhaps the most familiar news analysis system is Google News


Lydia Text Analysis System: Lydia Text Analysis System Lydia is designed for high-speed analysis of online text Lydia performs a variety of interesting analysis on named entities in text, breaking them down by source, location and time.


Block Diagram of Lydia System: Block Diagram of Lydia System


Process Involved: Process Involved Spidering and Article Classification Named Entity Recognition Juxtaposition Analysis Co-reference Set Identification Temporal and Spatial Analysis


News Analysis with Lydia: News Analysis with Lydia Juxtapositional Analysis. Spatial Analysis Temporal entity analysis


Juxtaposition Analysis: Juxtaposition Analysis Mental model of where an entity fits into the world depends largely upon how it relates to other entities. For each entity, we compute a significance score for every other entity that co-occurs with it, and rank its juxtapositions by this score.


Cont.: Cont. To determine the significance of a juxtaposition, they bound the probability that two entities co-occur in the number of articles that they co-occur in if occurrences where generated by a random process. To estimate this probability they use a Chernoff Bound:


Spatial Analysis: Spatial Analysis It is interesting to see where in the country people are talking about particular entities. Each newspaper has a location and a circulation and each city has a population. These facts allow them to approximate a sphere of influence for each newspaper. The heat on entity generated in a city is now a function of its frequency of reference in each of the newspapers that have influence over that city.


Cont.: Cont.


Temporal Analysis: Temporal Analysis Ability to track all references to entities broken down by article type gives the ability to monitor trends. Figure tracks the ebbs and flows in the interest in Michael Jackson as his trial progressed in May 2005.


How the paper is related to DM?: How the paper is related to DM? In the Lydia system in order to Classify the articles into different categories like news, sports etc., they use Bayesian classifier. Bayesian classifier is classification and prediction algorithm. Data Classification is DM technique which is done in two stages -building a model using predetermined set of data classes. -prediction of the input data.


Application: Application GIS (Geographical Information System)


What is GIS???: What is GIS??? A GIS is a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; Example: GIS might be used to find wetlands that need protection from pollution.


How does a GIS work?: How does a GIS work? GIS works by Relating information from different sources The power of a GIS comes from the ability to relate different information in a spatial context and to reach a conclusion about this relationship. Most of the information we have about our world contains a location reference, placing that information at some point on the globe.


Geological Survey (USGS) Digital Line Graph (DLG) of roads. : Geological Survey (USGS) Digital Line Graph (DLG) of roads.


Digital Line Graph of rivers. : Digital Line Graph of rivers.


Data capture: Data capture If the data to be used are not already in digital form - Maps can be digitized by hand-tracing with a computer mouse - Electronic scanners can also be used Co-ordinates for the maps can be collected using Global Positioning System (GPS) receivers Putting the information into the system—involves identifying the objects on the map, their absolute location on the Earth's surface, and their spatial relationships .


Data integration: Data integration A GIS makes it possible to link, or integrate, information that is difficult to associate through any other means. Mapmaking


Mapmaking: Mapmaking Researchers are working to incorporate the mapmaking processes of traditional cartographers into GIS technology for the automated production of maps.


What is special about GIS??: What is special about GIS?? Information retrieval: What do you know about the swampy area at the end of your street? With a GIS you can "point" at a location, object, or area on the screen and retrieve recorded information about it from off-screen files . Using scanned aerial photographs as a visual guide, you can ask a GIS about the geology or hydrology of the area or even about how close a swamp is to the end of a street. This type of analysis allows you to draw conclusions about the swamp's environmental sensitivity.


Cont.: Cont. Topological modeling: Have there ever been gas stations or factories that operated next to the swamp? Were any of these uphill from and within 2 miles of the swamp? A GIS can recognize and analyze the spatial relationships among mapped phenomena. Conditions of adjacency (what is next to what), containment (what is enclosed by what), and proximity (how close something is to something else) can be determined with a GIS


Cont.: Cont. Networks: When nutrients from farmland are running off into streams, it is important to know in which direction the streams flow and which streams empty into other streams. This is done by using a linear network. It allows the computer to determine how the nutrients are transported downstream. Additional information on water volume and speed throughout the spatial network can help the GIS determine how long it will take the nutrients to travel downstream


Data Output: Data Output A critical component of a GIS is its ability to produce graphics on the screen or on paper to convey the results of analyses to the people who make decisions about resources.


The future of GIS: The future of GIS GIS and related technology will help analyze large datasets, allowing a better understanding of terrestrial processes and human activities to improve economic vitality and environmental quality


How is it related to DM?: How is it related to DM? In order to represent the data in graphical Format which is most likely represented as a graph cluster analysis is done on the data set. Clustering is a data mining concept which is a process of grouping together the data into clusters or classes.


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