Module 2_ Introduction to Data Mining, Data Exploration and Data Pre-processing.pptx
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The document discusses data mining architecture, tasks, and data exploration and preprocessing techniques. It describes the KDD process and issues in data mining. It also covers data types, attributes, statistical descriptions of data, and various data visualization techniques like histograms, boxplots, scatter plots and quantile plots to explore patterns in data. Data preprocessing steps discussed are data cleaning, integration, transformation, reduction and discretization.
![63
Scatterplot Matrices
Matrix of scatterplots (x-y-diagrams) of the k-dim. data [total of (k2/2-k) scatterplots]
Used
by
ermission
of
M.
Ward,
Worcester
Polytechnic
Institute](https://image.slidesharecdn.com/module2introductiontodataminingdataexplorationanddatapre-processing-230827153635-49c58244/85/Module-2_-Introduction-to-Data-Mining-Data-Exploration-and-Data-Pre-processing-pptx-63-320.jpg)
![65
Parallel Coordinates
■ n equidistant axes which are parallel to one of the screen axes and
correspond to the attributes
■ The axes are scaled to the [minimum, maximum]: range of the
corresponding attribute
■ Every data item corresponds to a polygonal line which intersects each
of the axes at the point which corresponds to the value for the
attribute](https://image.slidesharecdn.com/module2introductiontodataminingdataexplorationanddatapre-processing-230827153635-49c58244/85/Module-2_-Introduction-to-Data-Mining-Data-Exploration-and-Data-Pre-processing-pptx-65-320.jpg)
![78
Similarity and Dissimilarity
■ Similarity
■ Numerical measure of how alike two data objects are
■ Value is higher when objects are more alike
■ Often falls in the range [0,1]
■ Dissimilarity (e.g., distance)
■ Numerical measure of how different two data objects are
■ Lower when objects are more alike
■ Minimum dissimilarity is often 0
■ Upper limit varies
■ Proximity refers to a similarity or dissimilarity](https://image.slidesharecdn.com/module2introductiontodataminingdataexplorationanddatapre-processing-230827153635-49c58244/85/Module-2_-Introduction-to-Data-Mining-Data-Exploration-and-Data-Pre-processing-pptx-78-320.jpg)
Module 2_ Introduction to Data Mining, Data Exploration and Data Pre-processing.pptx
- 1. Introduction to Data Mining, Data Exploration and Data Pre- processing CSC504.2 Understand data mining principles and perform Data preprocessing and Visualization. Prof. Nikhat Fatma Mumtaz Husain Shaikh
- 2. Contents Data Mining Task Primitives Architecture KDD process Issues in Data Mining Applications of Data Mining Data Exploration: Types of Attributes, Statistical Description of Data, Data Visualization Data Preprocessing: Descriptive data summarization, Cleaning, Integration & transformation, Data reduction Data Discretization and Concept hierarchy generation.
- 11. Knowledge base: This is the domain knowledge that is used to guide the search or evaluate the interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attributes or attribute values into different levels of abstraction. Data warehouses typically provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.
- 16. Data Mining Task Primitives
- 20. 20 Types of Data Sets ■ Record ■ Relational records ■ Data matrix, e.g., numerical matrix, crosstabs ■ Document data: text documents: term-frequency vector ■ Transaction data ■ Graph and network ■ World Wide Web ■ Social or information networks ■ Molecular Structures ■ Ordered ■ Video data: sequence of images ■ Temporal data: time-series ■ Sequential Data: transaction sequences ■ Genetic sequence data ■ Spatial, image and multimedia: ■ Spatial data: maps ■ Image data: ■ Video data:
- 21. 21 Important Characteristics of Structured Data ■ Dimensionality ■ Curse of dimensionality ■ Sparsity ■ Only presence counts ■ Resolution ■ Patterns depend on the scale ■ Distribution ■ Centrality and dispersion
- 22. 22 Data Objects ■ Data sets are made up of data objects. ■ A data object represents an entity. ■ Examples: ■ sales database: customers, store items, sales ■ medical database: patients, treatments ■ university database: students, professors, courses ■ Also called samples , examples, instances, data points, objects, tuples. ■ Data objects are described by attributes. ■ Database rows -> data objects; columns ->attributes.
- 23. 23 Attributes ■ Attribute (or dimensions, features, variables): a data field, representing a characteristic or feature of a data object. ■ E.g., customer _ID, name, address ■ Types: ■ Nominal ■ Binary ■ Numeric: quantitative ■ Interval-scaled ■ Ratio-scaled
- 24. 24 Attribute Types ■ Nominal: categories, states, or “names of things” ■ Hair_color = {auburn, black, blond, brown, grey, red, white} ■ marital status, occupation, ID numbers, zip codes ■ Binary ■ Nominal attribute with only 2 states (0 and 1) ■ Symmetric binary: both outcomes equally important ■ e.g., gender ■ Asymmetric binary: outcomes not equally important. ■ e.g., medical test (positive vs. negative) ■ Convention: assign 1 to most important outcome (e.g., HIV positive) ■ Ordinal ■ Values have a meaningful order (ranking) but magnitude between
- 25. 25 Numeric Attribute Types ■ Quantity (integer or real-valued) ■ Interval ■ Measured on a scale of equal-sized units ■ Values have order ■ E.g., temperature in C˚or F˚, calendar dates ■ No true zero-point ■ Ratio ■ Inherent zero-point ■ We can speak of values as being an order of magnitude larger than the unit of measurement (10 K˚ is twice as high as 5 K˚).
- 26. 26 Discrete vs. Continuous Attributes ■ Discrete Attribute ■ Has only a finite or countably infinite set of values ■ E.g., zip codes, profession, or the set of words in a collection of documents ■ Sometimes, represented as integer variables ■ Note: Binary attributes are a special case of discrete attributes ■ Continuous Attribute ■ Has real numbers as attribute values ■ E.g., temperature, height, or weight ■ Practically, real values can only be measured and represented using a finite number of digits ■ Continuous attributes are typically represented as floating-point variables
- 31. 31 Basic Statistical Descriptions of Data ■ 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
- 45. * Data Mining: Concepts and Techniques 45 Symmetric vs. Skewed Data ■ Median, mean and mode of symmetric, positively and negatively skewed data positively skewed negatively skewed symmetric
- 46. 46 Measuring the Dispersion of Data ■ Quartiles, outliers and boxplots ■ Quartiles: Q1 (25th percentile), Q3 (75th percentile) ■ Inter-quartile range: IQR = Q3 – Q1 ■ Five number summary: min, Q1, median, Q3, max ■ Boxplot: ends of the box are the quartiles; median is marked; add whiskers, and plot outliers individually ■ Outlier: usually, a value higher/lower than 1.5 x IQR ■ Variance and standard deviation (sample: s, population: σ) ■ Variance: (algebraic, scalable computation) ■ Standard deviation s (or σ) is the square root of variance s2 (or σ2)
- 47. 47 Boxplot Analysis ■ Five-number summary of a distribution ■ Minimum, Q1, Median, 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 IQR ■ The median is marked by a line within the box ■ Whiskers: two lines outside the box extended to Minimum and Maximum ■ Outliers: points beyond a specified outlier threshold, plotted individually
- 48. * Data Mining: Concepts and Techniques 48 Visualization of Data Dispersion: 3-D Boxplots
- 49. 49 Properties of Normal Distribution Curve ■ The normal (distribution) curve ■ From μ–σ to μ+σ: contains about 68% of the measurements (μ: mean, σ: standard deviation) ■ From μ–2σ to μ+2σ: contains about 95% of it ■ From μ–3σ to μ+3σ: contains about 99.7% of it
- 50. 50 Graphic Displays of Basic Statistical Descriptions ■ Boxplot: graphic display of five-number summary ■ Histogram: x-axis are values, y-axis repres. frequencies ■ Quantile plot: each value xi is paired with fi indicating that approximately 100 fi % of data are ≤ xi ■ 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
- 51. 51 Histogram Analysis ■ Histogram: Graph display of tabulated frequencies, shown as bars ■ It shows what proportion of cases fall into each of several categories ■ Differs from a bar chart in that it is the area of the bar that denotes the value, not the height as in bar charts, a crucial distinction when the categories are not of uniform width ■ The categories are usually specified as non- overlapping intervals of some variable. The categories (bars) must be adjacent
- 52. 52 Histograms Often Tell More than Boxplots ■ The two histograms shown in the left may have the same boxplot representation ■ The same values for: min, Q1, median, Q3, max ■ But they have rather different data distributions
- 53. Data Mining: Concepts and Techniques 53 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 xi data sorted in increasing order, fi indicates that approximately 100 fi% of the data are below or equal to the value xi
- 54. 54 Quantile-Quantile (Q-Q) Plot ■ Graphs the quantiles of one univariate distribution against the corresponding quantiles of another ■ View: Is there is a shift in going from one distribution to another? ■ Example shows unit price of items sold at Branch 1 vs. Branch 2 for each quantile. Unit prices of items sold at Branch 1 tend to be lower than those at Branch 2.
- 55. 55 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
- 56. 56 Positively and Negatively Correlated Data ■ The left half fragment is positively correlated ■ The right half is negative correlated
- 58. 58 Data Visualization ■ Why data visualization? ■ Gain insight into an information space by mapping data onto graphical primitives ■ Provide qualitative overview of large data sets ■ Search for patterns, trends, structure, irregularities, relationships among data ■ Help find interesting regions and suitable parameters for further quantitative analysis ■ Provide a visual proof of computer representations derived ■ Categorization of visualization methods: ■ Pixel-oriented visualization techniques ■ Geometric projection visualization techniques ■ Icon-based visualization techniques ■ Hierarchical visualization techniques ■ Visualizing complex data and relations
- 59. 59 Pixel-Oriented Visualization Techniques ■ For a data set of m dimensions, create m windows on the screen, one for each dimension ■ The m dimension values of a record are mapped to m pixels at the corresponding positions in the windows ■ The colors of the pixels reflect the corresponding values (a) Income (b) Credit Limit (c) transaction volume (d) age
- 60. 60 Laying Out Pixels in Circle Segments ■ To save space and show the connections among multiple dimensions, space filling is often done in a circle segment (a) Representing a data record in circle segment (b) Laying out pixels in circle segment
- 61. 61 Geometric Projection Visualization Techniques ■ Visualization of geometric transformations and projections of the data ■ Methods ■ Direct visualization ■ Scatterplot and scatterplot matrices ■ Landscapes ■ Projection pursuit technique: Help users find meaningful projections of multidimensional data ■ Prosection views ■ Hyperslice ■ Parallel coordinates
- 62. Data Mining: Concepts and Techniques 62 Direct Data Visualization Ribbons with Twists Based on Vorticity
- 63. 63 Scatterplot Matrices Matrix of scatterplots (x-y-diagrams) of the k-dim. data [total of (k2/2-k) scatterplots] Used by ermission of M. Ward, Worcester Polytechnic Institute
- 64. 64 news articles visualized as a landscape Used by permission of B. Wright, Visible Decisions Inc. Landscapes ■ Visualization of the data as perspective landscape ■ The data needs to be transformed into a (possibly artificial) 2D spatial representation which preserves the characteristics of the data
- 65. 65 Parallel Coordinates ■ n equidistant axes which are parallel to one of the screen axes and correspond to the attributes ■ The axes are scaled to the [minimum, maximum]: range of the corresponding attribute ■ Every data item corresponds to a polygonal line which intersects each of the axes at the point which corresponds to the value for the attribute
- 66. 66 Parallel Coordinates of a Data Set
- 67. 67 Icon-Based Visualization Techniques ■ Visualization of the data values as features of icons ■ Typical visualization methods ■ Chernoff Faces ■ Stick Figures ■ General techniques ■ Shape coding: Use shape to represent certain information encoding ■ Color icons: Use color icons to encode more information ■ Tile bars: Use small icons to represent the relevant feature vectors in document retrieval
- 68. 68 Chernoff Faces ■ A way to display variables on a two-dimensional surface, e.g., let x be eyebrow slant, y be eye size, z be nose length, etc. ■ The figure shows faces produced using 10 characteristics--head eccentricity, eye size, eye spacing, eye eccentricity, pupil size, eyebrow slant, nose size, mouth shape, mouth size, and mouth opening): Each assigned one of 10 possible values, generated using Mathematica (S. Dickson)
- 69. 69 Two attributes mapped to axes, remaining attributes mapped to angle or length of limbs”. Look at texture pattern A census data figure showing age, income, gender, education, etc. Stick Figure A 5-piece stick figure (1 body and 4 limbs w. different angle/length)
- 70. 70 Hierarchical Visualization Techniques ■ Visualization of the data using a hierarchical partitioning into subspaces ■ Methods ■ Dimensional Stacking ■ Worlds-within-Worlds ■ Tree-Map ■ Cone Trees ■ InfoCube
- 71. 71 Dimensional Stacking ■ Partitioning of the n-dimensional attribute space in 2-D subspaces, which are ‘stacked’ into each other ■ Partitioning of the attribute value ranges into classes. The important attributes should be used on the outer levels. ■ Adequate for data with ordinal attributes of low cardinality ■ But, difficult to display more than nine dimensions ■ Important to map dimensions appropriately
- 72. 72 Used by permission of M. Ward, Worcester Polytechnic Institute Visualization of oil mining data with longitude and latitude mapped to the outer x-, y-axes and ore grade and depth mapped to the inner x-, y-axes Dimensional Stacking
- 73. 73 Tree-Map ■ Screen-filling method which uses a hierarchical partitioning of the screen into regions depending on the attribute values ■ The x- and y-dimension of the screen are partitioned alternately according to the attribute values (classes) MSR Netscan Image Ack.: http://www.cs.umd.edu/hcil/treemap-history/all102001.jpg
- 74. 74 Tree-Map of a File System (Schneiderman)
- 75. 75 InfoCube ■ A 3-D visualization technique where hierarchical information is displayed as nested semi-transparent cubes ■ The outermost cubes correspond to the top level data, while the subnodes or the lower level data are represented as smaller cubes inside the outermost cubes, and so on
- 76. 76 Three-D Cone Trees ■ 3D cone tree visualization technique works well for up to a thousand nodes or so ■ First build a 2D circle tree that arranges its nodes in concentric circles centered on the root node ■ Cannot avoid overlaps when projected to 2D ■ G. Robertson, J. Mackinlay, S. Card. “Cone Trees: Animated 3D Visualizations of Hierarchical Information”, ACM SIGCHI'91 ■ Graph from Nadeau Software Consulting website: Visualize a social network data set that models the way an infection spreads from one person to the next Ack.: http://nadeausoftware.com/articles/visualization
- 77. Visualizing Complex Data and Relations ■ Visualizing non-numerical data: text and social networks ■ Tag cloud: visualizing user-generated tags ■ The importance of tag is represented by font size/color ■ Besides text data, there are also methods to visualize relationships, such as visualizing social networks Newsmap: Google News Stories in 2005
- 78. 78 Similarity and Dissimilarity ■ Similarity ■ Numerical measure of how alike two data objects are ■ Value is higher when objects are more alike ■ Often falls in the range [0,1] ■ Dissimilarity (e.g., distance) ■ Numerical measure of how different two data objects are ■ Lower when objects are more alike ■ Minimum dissimilarity is often 0 ■ Upper limit varies ■ Proximity refers to a similarity or dissimilarity
- 79. 79 Data Matrix and Dissimilarity Matrix ■ Data matrix ■ n data points with p dimensions ■ Two modes ■ Dissimilarity matrix ■ n data points, but registers only the distance ■ A triangular matrix ■ Single mode
- 80. 80 Proximity Measure for Nominal Attributes ■ Can take 2 or more states, e.g., red, yellow, blue, green (generalization of a binary attribute) ■ Method 1: Simple matching ■ m: # of matches, p: total # of variables ■ Method 2: Use a large number of binary attributes ■ creating a new binary attribute for each of the M nominal states
- 81. 81 Proximity Measure for Binary Attributes ■ A contingency table for binary data ■ Distance measure for symmetric binary variables: ■ Distance measure for asymmetric binary variables: ■ Jaccard coefficient (similarity measure for asymmetric binary variables): ■ Note: Jaccard coefficient is the same as “coherence”: Object i Object j
- 82. 82 Standardizing Numeric Data ■ Z-score: ■ X: raw score to be standardized, μ: mean of the population, σ: standard deviation ■ the distance between the raw score and the population mean in units of the standard deviation ■ negative when the raw score is below the mean, “+” when above ■ An alternative way: Calculate the mean absolute deviation where ■ standardized measure (z-score): ■ Using mean absolute deviation is more robust than using standard deviation