Data Mining Concept Description 9122021 Data Mining Concepts

Data Mining Concept Description 9/12/2021 Data Mining: Concepts and Techniques 1

Chapter 5: Concept Description: Characterization and Comparison n n What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Summary 9/12/2021 Data Mining: Concepts and Techniques 2

What is Concept Description? n n Descriptive vs. predictive data mining n Descriptive mining: describes concepts or task-relevant data sets in concise, summarative, informative, discriminative forms n Predictive mining: Based on data and analysis, constructs models for the database, and predicts the trend and properties of unknown data Concept description: n Characterization: provides a concise and succinct summarization of the given collection of data n Comparison: provides descriptions comparing two or more collections of data

Concept Description n n What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Summary 9/12/2021 Data Mining: Concepts and Techniques 4

Data Generalization and Summarizationbased Characterization n Data generalization n A process which abstracts a large set of task-relevant data in a database from a low conceptual levels to higher ones. 1 2 3 4 5 n 9/12/2021 Conceptual levels Approaches: n Data cube approach(OLAP approach) n Attribute-oriented induction approach Data Mining: Concepts and Techniques 5

Characterization: Data Cube Approach (without using AO-Induction) n Perform computations and store results in data cubes n Strength n An efficient implementation of data generalization n Computation of various kinds of measures n n n e. g. , count( ), sum( ), average( ), max( ) Generalization and specialization can be performed on a data cube by roll-up and drill-down Limitations n n 9/12/2021 handle only dimensions of simple nonnumeric data and measures of simple aggregated numeric values. Lack of intelligent analysis, can’t tell which dimensions should be used and what levels should the generalization reach Data Mining: Concepts and Techniques 6

Attribute-Oriented Induction n n Not confined to categorical data nor particular measures. How it is done? n Collect the task-relevant data (initial relation) using a relational database query n Perform generalization by attribute removal or attribute generalization. n Apply aggregation by merging identical, generalized tuples and accumulating their respective counts. n Interactive presentation with users. 9/12/2021 Data Mining: Concepts and Techniques 7

Basic Principles of Attribute. Oriented Induction n Data focusing: task-relevant data, including dimensions, and the result is the initial relation. Attribute-removal: remove attribute A if there is a large set of distinct values for A but (1) there is no generalization operator on A, or (2) A’s higher level concepts are expressed in terms of other attributes. Attribute-generalization: If there is a large set of distinct values for A, and there exists a set of generalization operators on A, then select an operator and generalize A. n n Attribute-threshold control: typical 2 -8, specified/default. Generalized relation threshold control: control the final relation/rule size.

Basic Algorithm for Attribute-Oriented Induction n n Initial. Rel: Query processing of task-relevant data, deriving the initial relation. Pre. Gen: Based on the analysis of the number of distinct values in each attribute, determine generalization plan for each attribute: removal? or how high to generalize? Prime. Gen: Based on the Pre. Gen plan, perform generalization to the right level to derive a “prime generalized relation”, accumulating the counts. Presentation: User interaction e. g. adjust levels

Class Characterization: An Example Initial Relation Prime Generalized Relation

Presentation of Generalized Results n Generalized relation: n n Cross tabulation: n n Mapping results into cross tabulation form (similar to contingency tables). Visualization techniques: n n Relations where some or all attributes are generalized, with counts or other aggregation values accumulated. Pie charts, bar charts, curves, cubes, and other visual forms. Quantitative characteristic rules: n Mapping generalized result into characteristic rules with quantitative information associated with it, e. g. ,

Presentation—Generalized Relation 9/12/2021 Data Mining: Concepts and Techniques 12

Presentation—Crosstab 9/12/2021 Data Mining: Concepts and Techniques 13

Concept Description n n What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Summary 9/12/2021 Data Mining: Concepts and Techniques 14

Relevance Measures n n 9/12/2021 Quantitative relevance measure determines the classifying power of an attribute within a set of data. Methods n information gain (ID 3) n gain ratio (C 4. 5) n gini index 2 n contingency table statistics n uncertainty coefficient Data Mining: Concepts and Techniques 15

Top-Down Induction of Decision Tree Attributes = {Outlook, Temperature, Humidity, Wind} Play. Tennis = {yes, no} Outlook sunny overcast Humidity high no 9/12/2021 rain Wind yes normal yes strong no Data Mining: Concepts and Techniques weak yes 16

Example: Analytical Characterization n n 9/12/2021 Task n Mine general characteristics describing graduate students using analytical characterization Given n attributes name, gender, major, birth_place, birth_date, phone#, and gpa n Gen(ai) = concept hierarchies on ai n Ti = attribute generalization thresholds for ai n R = attribute relevance threshold Data Mining: Concepts and Techniques 17

Example: Analytical Characterization (cont’d) n n 1. Data collection n target class: graduate student n contrasting class: undergraduate student 2. Analytical generalization n attribute removal n n attribute generalization n 9/12/2021 remove name and phone# generalize major, birth_place, birth_date and gpa accumulate counts candidate relation: gender, major, birth_country, age_range and gpa Data Mining: Concepts and Techniques 18

Example: Analytical characterization (2) Candidate relation for Target class: Graduate students ( =120) Candidate relation for Contrasting class: Undergraduate students ( =130) 9/12/2021 Data Mining: Concepts and Techniques 19

Example: Analytical characterization (3) n 4. Initial working relation (W 0) derivation n R = 0. 1 remove irrelevant/weakly relevant attributes from candidate relation => drop gender, birth_country remove contrasting class candidate relation Initial target class working relation W 0: Graduate students n 9/12/2021 5. Perform attribute-oriented induction on W 0 using Ti Data Mining: Concepts and Techniques 20

Quantitative Discriminant Rules n n 9/12/2021 Cj = target class qa = a generalized tuple covers some tuples of class n but can also cover some tuples of contrasting class d-weight n range: [0, 1] quantitative discriminant rule form Data Mining: Concepts and Techniques 21

Example: Quantitative Discriminant Rule Count distribution between graduate and undergraduate students for a generalized tuple n Quantitative discriminant rule n 9/12/2021 where 90/(90+210) = 30% Data Mining: Concepts and Techniques 22

Class Description n Quantitative characteristic rule n necessary Quantitative discriminant rule n sufficient Quantitative description rule n n n 9/12/2021 necessary and sufficient Data Mining: Concepts and Techniques 23

Example: Quantitative Description Rule Crosstab showing associated t-weight, d-weight values and total number (in thousands) of TVs and computers sold at All. Electronics in 1998 n 9/12/2021 Quantitative description rule for target class Europe Data Mining: Concepts and Techniques 24

Chapter 5: Concept Description: Characterization and Comparison n n What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Summary 9/12/2021 Data Mining: Concepts and Techniques 25

Summary n Concept description: characterization and discrimination n OLAP-based vs. attribute-oriented induction n Efficient implementation of AOI n Analytical characterization 9/12/2021 Data Mining: Concepts and Techniques 26