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Chapter 5: Concept Description: Characterization and Comparison:

February 27, 2011 Data Mining: Concepts and Techniques 1 Chapter 5: Concept Description: Characterization and Comparison What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Mining descriptive statistical measures in large databases Discussion Summary

What is Concept Description?:

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

Concept Description vs. OLAP:

February 27, 2011 Data Mining: Concepts and Techniques 3 Concept Description vs. OLAP Concept description: can handle complex data types of the attributes and their aggregations a more automated process OLAP: restricted to a small number of dimension and measure types user-controlled process

Chapter 5: Concept Description: Characterization and Comparison:

February 27, 2011 Data Mining: Concepts and Techniques 4 Chapter 5: Concept Description: Characterization and Comparison What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Mining descriptive statistical measures in large databases Discussion Summary

Data Generalization and Summarization-based Characterization:

February 27, 2011 Data Mining: Concepts and Techniques 5 Data Generalization and Summarization-based Characterization Data generalization A process which abstracts a large set of task-relevant data in a database from a low conceptual levels to higher ones. Approaches: Data cube approach(OLAP approach) Attribute-oriented induction approach 1 2 3 4 5 Conceptual levels

Characterization: Data Cube Approach (without using AO-Induction):

February 27, 2011 Data Mining: Concepts and Techniques 6 Characterization: Data Cube Approach (without using AO-Induction) Perform computations and store results in data cubes Strength An efficient implementation of data generalization Computation of various kinds of measures e.g., count( ), sum( ), average( ), max( ) Generalization and specialization can be performed on a data cube by roll-up and drill-down Limitations 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

Attribute-Oriented Induction:

February 27, 2011 Data Mining: Concepts and Techniques 7 Attribute-Oriented Induction Proposed in 1989 (KDD ‘89 workshop) Not confined to categorical data nor particular measures. How it is done? Collect the task-relevant data( initial relation ) using a relational database query Perform generalization by attribute removal or attribute generalization . Apply aggregation by merging identical, generalized tuples and accumulating their respective counts. Interactive presentation with users.

Basic Principles of Attribute-Oriented Induction:

Basic Principles of Attribute-Oriented Induction 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 . Attribute-threshold control : typical 2-8, specified/default. Generalized relation threshold control : control the final relation/rule size. see example

Basic Algorithm for Attribute-Oriented Induction:

Basic Algorithm for Attribute-Oriented Induction InitialRel : Query processing of task-relevant data, deriving the initial relation . PreGen : 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? PrimeGen : Based on the PreGen plan, perform generalization to the right level to derive a “prime generalized relation”, accumulating the counts. Presentation : User interaction: (1) adjust levels by drilling, (2) pivoting, (3) mapping into rules, cross tabs, visualization presentations. See Implementation See example See complexity

Example:

February 27, 2011 Data Mining: Concepts and Techniques 10 Example DMQL : Describe general characteristics of graduate students in the Big-University database use Big_University_DB mine characteristics as “Science_Students” in relevance to name, gender, major, birth_place, birth_date, residence, phone#, gpa from student where status in “graduate” Corresponding SQL statement: Select name, gender, major, birth_place, birth_date, residence, phone#, gpa from student where status in {“Msc”, “MBA”, “PhD” }

Class Characterization: An Example:

Class Characterization: An Example See Principles See Algorithm Prime Generalized Relation Initial Relation See Implementation See Analytical Characterization

Presentation of Generalized Results:

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

Presentation—Generalized Relation:

February 27, 2011 Data Mining: Concepts and Techniques 13 Presentation — Generalized Relation

Presentation—Crosstab:

February 27, 2011 Data Mining: Concepts and Techniques 14 Presentation — Crosstab

Implementation by Cube Technology:

February 27, 2011 Data Mining: Concepts and Techniques 15 Implementation by Cube Technology Construct a data cube on-the-fly for the given data mining query Facilitate efficient drill-down analysis May increase the response time A balanced solution: precomputation of “subprime” relation Use a predefined & precomputed data cube Construct a data cube beforehand Facilitate not only the attribute-oriented induction, but also attribute relevance analysis, dicing, slicing, roll-up and drill-down Cost of cube computation and the nontrivial storage overhead

Chapter 5: Concept Description: Characterization and Comparison:

February 27, 2011 Data Mining: Concepts and Techniques 16 Chapter 5: Concept Description: Characterization and Comparison What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Mining descriptive statistical measures in large databases Discussion Summary

Characterization vs. OLAP:

February 27, 2011 Data Mining: Concepts and Techniques 17 Characterization vs. OLAP Similarity: Presentation of data summarization at multiple levels of abstraction. Interactive drilling, pivoting, slicing and dicing. Differences: Automated desired level allocation. Dimension relevance analysis and ranking when there are many relevant dimensions. Sophisticated typing on dimensions and measures. Analytical characterization: data dispersion analysis.

Attribute Relevance Analysis:

February 27, 2011 Data Mining: Concepts and Techniques 18 Attribute Relevance Analysis Why? Which dimensions should be included? How high level of generalization? Automatic vs. interactive Reduce # attributes; easy to understand patterns What? statistical method for preprocessing data filter out irrelevant or weakly relevant attributes retain or rank the relevant attributes relevance related to dimensions and levels analytical characterization, analytical comparison

Attribute relevance analysis (cont’d):

February 27, 2011 Data Mining: Concepts and Techniques 19 Attribute relevance analysis (cont’d) How? Data Collection Analytical Generalization Use information gain analysis (e.g., entropy or other measures) to identify highly relevant dimensions and levels. Relevance Analysis Sort and select the most relevant dimensions and levels. Attribute-oriented Induction for class description On selected dimension/level OLAP operations (e.g. drilling, slicing) on relevance rules

Relevance Measures :

February 27, 2011 Data Mining: Concepts and Techniques 20 Relevance Measures Quantitative relevance measure determines the classifying power of an attribute within a set of data. Methods information gain (ID3) gain ratio (C4.5) gini index  2 contingency table statistics uncertainty coefficient

Information-Theoretic Approach:

February 27, 2011 Data Mining: Concepts and Techniques 21 Information-Theoretic Approach Decision tree each internal node tests an attribute each branch corresponds to attribute value each leaf node assigns a classification ID3 algorithm build decision tree based on training objects with known class labels to classify testing objects rank attributes with information gain measure minimal height the least number of tests to classify an object See example

Top-Down Induction of Decision Tree:

February 27, 2011 Data Mining: Concepts and Techniques 22 Top-Down Induction of Decision Tree Attributes = {Outlook, Temperature, Humidity, Wind} Outlook Humidity Wind sunny rain overcast yes no yes high normal no strong weak yes PlayTennis = {yes, no}

Entropy and Information Gain:

February 27, 2011 Data Mining: Concepts and Techniques 23 Entropy and Information Gain S contains s i tuples of class C i for i = {1, …, m} Information measures info required to classify any arbitrary tuple Entropy of attribute A with values {a 1 ,a 2 ,…,a v } Information gained by branching on attribute A

Example: Analytical Characterization:

February 27, 2011 Data Mining: Concepts and Techniques 24 Example: Analytical Characterization Task Mine general characteristics describing graduate students using analytical characterization Given attributes name, gender, major, birth_place, birth_date, phone# , and gpa Gen(a i ) = concept hierarchies on a i U i = attribute analytical thresholds for a i T i = attribute generalization thresholds for a i R = attribute relevance threshold

Example: Analytical Characterization (cont’d):

February 27, 2011 Data Mining: Concepts and Techniques 25 Example: Analytical Characterization (cont’d) 1. Data collection target class: graduate student contrasting class: undergraduate student 2. Analytical generalization using U i attribute removal remove name and phone# attribute generalization generalize major , birth_place , birth_date and gpa accumulate counts candidate relation : gender , major , birth_country , age_range and gpa

Example: Analytical characterization (2):

February 27, 2011 Data Mining: Concepts and Techniques 26 Example: Analytical characterization (2) Candidate relation for Target class: Graduate students ( =120) Candidate relation for Contrasting class: Undergraduate students ( =130)

Example: Analytical characterization (3):

February 27, 2011 Data Mining: Concepts and Techniques 27 Example: Analytical characterization (3) 3. Relevance analysis Calculate expected info required to classify an arbitrary tuple Calculate entropy of each attribute: e.g. major Number of grad students in “Science” Number of undergrad students in “Science”

Example: Analytical Characterization (4):

February 27, 2011 Data Mining: Concepts and Techniques 28 Example: Analytical Characterization (4) Calculate expected info required to classify a given sample if S is partitioned according to the attribute Calculate information gain for each attribute Information gain for all attributes

Example: Analytical characterization (5):

February 27, 2011 Data Mining: Concepts and Techniques 29 Example: Analytical characterization (5) 4. Initial working relation (W 0 ) derivation R = 0.1 remove irrelevant/weakly relevant attributes from candidate relation => drop gender , birth_country remove contrasting class candidate relation 5. Perform attribute-oriented induction on W 0 using T i Initial target class working relation W 0 : Graduate students

Chapter 5: Concept Description: Characterization and Comparison:

February 27, 2011 Data Mining: Concepts and Techniques 30 Chapter 5: Concept Description: Characterization and Comparison What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Mining descriptive statistical measures in large databases Discussion Summary

Mining Class Comparisons:

Mining Class Comparisons Comparison: Comparing two or more classes. Method: Partition the set of relevant data into the target class and the contrasting class(es) Generalize both classes to the same high level concepts Compare tuples with the same high level descriptions Present for every tuple its description and two measures: support - distribution within single class comparison - distribution between classes Highlight the tuples with strong discriminant features Relevance Analysis: Find attributes (features) which best distinguish different classes.

Example: Analytical comparison:

February 27, 2011 Data Mining: Concepts and Techniques 32 Example: Analytical comparison Task Compare graduate and undergraduate students using discriminant rule. DMQL query use Big_University_DB mine comparison as “grad_vs_undergrad_students” in relevance to name, gender, major, birth_place, birth_date, residence, phone#, gpa for “graduate_students” where status in “graduate” versus “undergraduate_students” where status in “undergraduate” analyze count% from student

Example: Analytical comparison (2):

February 27, 2011 Data Mining: Concepts and Techniques 33 Example: Analytical comparison (2) Given attributes name, gender, major, birth_place, birth_date, residence, phone# and gpa Gen(a i ) = concept hierarchies on attributes a i U i = attribute analytical thresholds for attributes a i T i = attribute generalization thresholds for attributes a i R = attribute relevance threshold

Example: Analytical comparison (3):

February 27, 2011 Data Mining: Concepts and Techniques 34 Example: Analytical comparison (3) 1. Data collection target and contrasting classes 2. Attribute relevance analysis remove attributes name, gender, major, phone# 3. Synchronous generalization controlled by user-specified dimension thresholds prime target and contrasting class(es) relations/cuboids

Example: Analytical comparison (4):

February 27, 2011 Data Mining: Concepts and Techniques 35 Example: Analytical comparison (4) Prime generalized relation for the target class: Graduate students Prime generalized relation for the contrasting class: Undergraduate students

Example: Analytical comparison (5):

February 27, 2011 Data Mining: Concepts and Techniques 36 Example: Analytical comparison (5) 4. Drill down, roll up and other OLAP operations on target and contrasting classes to adjust levels of abstractions of resulting description 5. Presentation as generalized relations, crosstabs, bar charts, pie charts, or rules contrasting measures to reflect comparison between target and contrasting classes e.g. count%

Quantitative Discriminant Rules:

February 27, 2011 Data Mining: Concepts and Techniques 37 Quantitative Discriminant Rules Cj = target class q a = a generalized tuple covers some tuples of class but can also cover some tuples of contrasting class d-weight range: [0, 1] quantitative discriminant rule form

Example: Quantitative Discriminant Rule:

February 27, 2011 Data Mining: Concepts and Techniques 38 Example: Quantitative Discriminant Rule Quantitative discriminant rule where 90/(90+120) = 30% Count distribution between graduate and undergraduate students for a generalized tuple

Class Description :

February 27, 2011 Data Mining: Concepts and Techniques 39 Class Description Quantitative characteristic rule necessary Quantitative discriminant rule sufficient Quantitative description rule necessary and sufficient

Example: Quantitative Description Rule:

February 27, 2011 Data Mining: Concepts and Techniques 40 Example: Quantitative Description Rule Quantitative description rule for target class Europe Crosstab showing associated t-weight, d-weight values and total number (in thousands) of TVs and computers sold at AllElectronics in 1998

Chapter 5: Concept Description: Characterization and Comparison:

February 27, 2011 Data Mining: Concepts and Techniques 41 Chapter 5: Concept Description: Characterization and Comparison What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Mining descriptive statistical measures in large databases Discussion Summary

Mining Data Dispersion Characteristics:

February 27, 2011 Data Mining: Concepts and Techniques 42 Mining Data Dispersion Characteristics Motivation To better understand the data: central tendency, variation and spread Data dispersion characteristics median, max, min, quantiles, outliers, variance, etc. Numerical dimensions correspond to sorted intervals Data dispersion: analyzed with multiple granularities of precision Boxplot or quantile analysis on sorted intervals Dispersion analysis on computed measures Folding measures into numerical dimensions Boxplot or quantile analysis on the transformed cube

Measuring the Central Tendency:

February 27, 2011 Data Mining: Concepts and Techniques 43 Measuring the Central Tendency Mean Weighted arithmetic mean Median : A holistic measure Middle value if odd number of values, or average of the middle two values otherwise estimated by interpolation Mode Value that occurs most frequently in the data Unimodal, bimodal, trimodal Empirical formula:

Measuring the Dispersion of Data:

February 27, 2011 Data Mining: Concepts and Techniques 44 Measuring the Dispersion of Data Quartiles, outliers and boxplots Quartiles : Q 1 (25 th percentile), Q 3 (75 th percentile) Inter-quartile range : IQR = Q 3 – Q 1 Five number summary : min, Q 1 , M, Q 3 , max Boxplot : ends of the box are the quartiles, median is marked, whiskers, and plot outlier individually Outlier : usually, a value higher/lower than 1.5 x IQR Variance and standard deviation Variance s 2 : (algebraic, scalable computation) Standard deviation s is the square root of variance s 2

Boxplot Analysis:

February 27, 2011 Data Mining: Concepts and Techniques 45 Boxplot Analysis Five-number summary of a distribution: Minimum, Q1, M, Q3, Maximum Boxplot Data is represented with a box The ends of the box are at the first and third quartiles, i.e., the height of the box is IRQ The median is marked by a line within the box Whiskers: two lines outside the box extend to Minimum and Maximum

A Boxplot:

February 27, 2011 Data Mining: Concepts and Techniques 46 A Boxplot A boxplot

Visualization of Data Dispersion: Boxplot Analysis:

February 27, 2011 Data Mining: Concepts and Techniques 47 Visualization of Data Dispersion: Boxplot Analysis

Mining Descriptive Statistical Measures in Large Databases:

February 27, 2011 Data Mining: Concepts and Techniques 48 Mining Descriptive Statistical Measures in Large Databases Variance Standard deviation : the square root of the variance Measures spread about the mean It is zero if and only if all the values are equal Both the deviation and the variance are algebraic

Histogram Analysis:

February 27, 2011 Data Mining: Concepts and Techniques 49 Histogram Analysis Graph displays of basic statistical class descriptions Frequency histograms A univariate graphical method Consists of a set of rectangles that reflect the counts or frequencies of the classes present in the given data

Quantile Plot:

February 27, 2011 Data Mining: Concepts and Techniques 50 Quantile Plot Displays all of the data (allowing the user to assess both the overall behavior and unusual occurrences) Plots quantile information For a data x i data sorted in increasing order, f i indicates that approximately 100 f i % of the data are below or equal to the value x i

Quantile-Quantile (Q-Q) Plot:

February 27, 2011 Data Mining: Concepts and Techniques 51 Quantile-Quantile (Q-Q) Plot Graphs the quantiles of one univariate distribution against the corresponding quantiles of another Allows the user to view whether there is a shift in going from one distribution to another

Scatter plot:

February 27, 2011 Data Mining: Concepts and Techniques 52 Scatter plot Provides a first look at bivariate data to see clusters of points, outliers, etc Each pair of values is treated as a pair of coordinates and plotted as points in the plane

Loess Curve:

February 27, 2011 Data Mining: Concepts and Techniques 53 Loess Curve Adds a smooth curve to a scatter plot in order to provide better perception of the pattern of dependence Loess curve is fitted by setting two parameters: a smoothing parameter, and the degree of the polynomials that are fitted by the regression

Graphic Displays of Basic Statistical Descriptions:

February 27, 2011 Data Mining: Concepts and Techniques 54 Graphic Displays of Basic Statistical Descriptions Histogram: (shown before) Boxplot: (covered before) Quantile plot: each value x i is paired with f i indicating that approximately 100 f i % of data are  x i Quantile-quantile (q-q) plot: graphs the quantiles of one univariant distribution against the corresponding quantiles of another Scatter plot: each pair of values is a pair of coordinates and plotted as points in the plane Loess (local regression) curve: add a smooth curve to a scatter plot to provide better perception of the pattern of dependence

Chapter 5: Concept Description: Characterization and Comparison:

February 27, 2011 Data Mining: Concepts and Techniques 55 Chapter 5: Concept Description: Characterization and Comparison What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Mining descriptive statistical measures in large databases Discussion Summary

AO Induction vs. Learning-from-example Paradigm:

February 27, 2011 Data Mining: Concepts and Techniques 56 AO Induction vs. Learning-from-example Paradigm Difference in philosophies and basic assumptions Positive and negative samples in learning-from-example: positive used for generalization, negative - for specialization Positive samples only in data mining: hence generalization-based, to drill-down backtrack the generalization to a previous state Difference in methods of generalizations Machine learning generalizes on a tuple by tuple basis Data mining generalizes on an attribute by attribute basis

Comparison of Entire vs. Factored Version Space:

February 27, 2011 Data Mining: Concepts and Techniques 57 Comparison of Entire vs. Factored Version Space

Incremental and Parallel Mining of Concept Description:

February 27, 2011 Data Mining: Concepts and Techniques 58 Incremental and Parallel Mining of Concept Description Incremental mining: revision based on newly added data DB Generalize DB to the same level of abstraction in the generalized relation R to derive R Union R U R, i.e., merge counts and other statistical information to produce a new relation R’ Similar philosophy can be applied to data sampling, parallel and/or distributed mining, etc.

Chapter 5: Concept Description: Characterization and Comparison:

February 27, 2011 Data Mining: Concepts and Techniques 59 Chapter 5: Concept Description: Characterization and Comparison What is concept description? Data generalization and summarization-based characterization Analytical characterization: Analysis of attribute relevance Mining class comparisons: Discriminating between different classes Mining descriptive statistical measures in large databases Discussion Summary

Summary:

February 27, 2011 Data Mining: Concepts and Techniques 60 Summary Concept description: characterization and discrimination OLAP-based vs. attribute-oriented induction Efficient implementation of AOI Analytical characterization and comparison Mining descriptive statistical measures in large databases Discussion Incremental and parallel mining of description Descriptive mining of complex types of data

References:

February 27, 2011 Data Mining: Concepts and Techniques 61 References Y. Cai, N. Cercone, and J. Han. Attribute-oriented induction in relational databases. In G. Piatetsky-Shapiro and W. J. Frawley, editors, Knowledge Discovery in Databases, pages 213-228. AAAI/MIT Press, 1991. S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26:65-74, 1997 C. Carter and H. Hamilton. Efficient attribute-oriented generalization for knowledge discovery from large databases. IEEE Trans. Knowledge and Data Engineering, 10:193-208, 1998. W. Cleveland. Visualizing Data. Hobart Press, Summit NJ, 1993. J. L. Devore. Probability and Statistics for Engineering and the Science, 4th ed. Duxbury Press, 1995. T. G. Dietterich and R. S. Michalski. A comparative review of selected methods for learning from examples. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, pages 41-82. Morgan Kaufmann, 1983. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1:29-54, 1997. J. Han, Y. Cai, and N. Cercone. Data-driven discovery of quantitative rules in relational databases. IEEE Trans. Knowledge and Data Engineering, 5:29-40, 1993.

References (cont.):

February 27, 2011 Data Mining: Concepts and Techniques 62 References (cont.) J. Han and Y. Fu. Exploration of the power of attribute-oriented induction in data mining. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 399-421. AAAI/MIT Press, 1996. R. A. Johnson and D. A. Wichern. Applied Multivariate Statistical Analysis, 3rd ed. Prentice Hall, 1992. E. Knorr and R. Ng. Algorithms for mining distance-based outliers in large datasets. VLDB'98, New York, NY, Aug. 1998. H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, 1983. T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. IJCAI'97, Cambridge, MA. T. M. Mitchell. Generalization as search. Artificial Intelligence, 18:203-226, 1982. T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. D. Subramanian and J. Feigenbaum. Factorization in experiment generation. AAAI'86, Philadelphia, PA, Aug. 1986.

http://www.cs.sfu.ca/~han/dmbook:

February 27, 2011 Data Mining: Concepts and Techniques 63 http://www.cs.sfu.ca/~han/dmbook Thank you !!!