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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Chapter Three Data, pre-processing and exploration

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Chapter Overview Data, data types and operations Properties of various data sets Data source and data warehouse Issues of data quality Data pre-processing operations Data summary and visualisation Online analytic processing (OLAP) Data exploration and visualisation in Weka

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data, Data Types and Operations Data object and attributes –Data object or instance: individual independent recording of a real life object/event. –Characterised by its recorded values on a fixed set of features or attributes –Feature or attribute: a specific property or characteristic of the data object. –Measurement: assigning a valid value to an attribute according to an appropriate measurement scale. –Collection: collecting measurement results or recorded values

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data, Data Types and Operations Data object and attributes (cont’d) –An example 123, “John Smith”, “03/02/1990”, 20, “male”, 1.82, 78 ID number, collected Name collected Birthday collected Age calculated Gender collected Body height measured Body weight measured

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data, Data Types and Operations Data object and attributes (cont’d) –Measurement and measurement errors Precision: the closeness of measurements to one another, represented by the standard deviation of the measurements, e.g. repeated measure of body temperature Bias: a systematic variation of measurements from the intended quantity measurement, only known when external reference available, e.g. bias in weight measure instrument Accuracy: the closeness of the measure to the true value, indicated by the number of significant digits used in the measurement, e.g. measure of money: pound vs. penny –Collection errors Incorrect data recording at the point of entry, e.g. “Hongpo Do” as for “Hongbo Du”

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data, Data Types and Operations Attribute domain types and operations –Categorical/Qualitative types Nominal, e.g. Gender (M, F) –A set of names: no concept of order nor difference –Operators applicable: =, –1:1 transformation permissible, e.g. ID: 11 e901 Ordinal, e.g. Grade (A, B, C, D, E) –A set of names: with order but no concept of difference –Operator applicable: =, ,, , –Order-preserving transformation permitted, e.g. Grade: A First, B Second, C Third, D Pass, E BarePass.

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data, Data Types and Operations Attribute domain types and operations –Numeric/Quantitative types Interval, e.g. Temperature in C –A set of numeric values: both order and difference exist –Operators applicable: =, ,, , , +, - –e.g. temperature ( F and C), calendar year –Transformation new = a*old + b permitted, e.g. F C Ratio, e.g. Length –A set of numeric values: order, difference and ratio –The set has an absolute zero –Operator applicable: =, ,, , , +, -, , –Transformation new = a*old permitted, e.g. meter feet

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Sets Various forms –Table of records Relational table Join of relational tables Numerical spreadsheet (data matrix) Boolean strings (document-term matrix) –Ordered data Time series and temporal sequence Data sequence Spatial data –Graph-based data –Non record-based data

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Sets Various forms (illustrated) Relational Table Transaction Database Data Matrix Page1 link1 link2 Page2 link3 Page4 www zzzz Page3 xxxx yyyy Web Structure GGTTCCGCCTTCAGCC CCGCGCCCGCAGGG… Data Sequence Spatial Data

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Sets Properties –Type: file structure, e.g. ARFF for Weka, DAT for See5 –Size: measured in terms of the total number of records or total number of bytes, e.g. small (MB), medium (GB) and large (TB) –Dimensionality: number of attributes –Sparsity: Values are skewed to some extreme or sub-ranges Asymmetric values (some are more important than others) –Resolution Right level of data details Related to the intended purpose

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Sets Properties (example insurance data set) Type: ARFF Size: 14722 records Dimensionality: 7 Asymmetric: Y/N Skewed? Resolution: detailed

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Source and Data Warehouse Sources of data –Local data source available –Local operational systems from different departments –Third-party external data source –Enterprise/Organisational data warehouse An organisational database for decision making A central data repository separate from operational systems Enforcing organisation-wide data consistency and integration Providing data details as well as data summarisation Providing data values as well as meta-data Equipped with data analysis and reporting tools As a data source for data mining

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Source and Data Warehouse Star schema for data warehouse –Central fact table –Dimension tables –Limited use of join operations Part(p#, pname, weight, colour) Supplier(s#, sname, city, status) Project(pj#, jname, status, date) Supply(s#, p#, pj#, qty)

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Issues of Data Quality Main quality indicators –Accuracy: data recorded with sufficient precision and little bias –Correctness: data recorded without error and spurious objects –Completeness: any parts of data records missing –Consistency: compliance with established rules and constraints –Redundancy: unnecessary duplicates Using the indicators to quantify quality of a data set Improving quality if possible

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Issues of Data Quality Some examples –Accuracy & correctness with the road accident reports in Exercise 1.3(c). –Completeness with the UK family expenditure surveys in Exercise 1.3(a). –Incompleteness introduced by data integration using outer join operation –Consistency in questionnaires, e.g. eating fruit & veg. Q1: “give the fruit&veg portion consumed yesterday”: 2 Q2: “give the fruit&veg portion consumed today:” 3 Q3: “do you eat more today than yesterday?” No. –Redundancy in a local company’s database of 40,000 records about 15,000 client companies.

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Issues of Data Quality Why is quality important? –“Garbage in, garbage out!” –Total data quality control requires a cultural change (comparing with total product quality control) –For data mining, tackling the quality issue at the data source cannot be always expected By cleaning the data as much as possible By developing and using more tolerate mining solutions –Data quality is relevant to the intended purpose of data mining, e.g. Do spelling errors in student names really matter when only the increase/decrease of student numbers in particular subject areas over the years is of interest?

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Overview –Purpose: for speedy, cost-effective and high quality outcomes of data mining –Pre-processing tasks (not all are independent from each other) Data aggregation Data sampling Dimension reduction Feature selection Feature creation Discretisation/binarisation Variable transformation Dealing with missing values

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Data aggregation –What: to summarise low level data details to higher level data abstraction –Why: to reduce the time of mining, to rescale data values, and to discover more stable patterns –How: By generalisation using a given concept hierarchy By applying aggregate functions (e.g. count, sum, average) Dropping some attributes

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Data sampling –What: selecting a subset of the given data set –Why: to make it possible to use sophisticated mining algorithms within a time limit. –Caution: the sample must be representative of the original data set –How: Random sampling Stratified sampling Progressive sampling With or without replacement Data population Selected subset Sampling method

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Feature selection –What: reducing dimensionality by selecting a subset of attributes –Purposes: To remove/reduce redundant features To remove irrelevant features with no useful information for the mining task –How: Manually with common sense and domain knowledge Letting the mining solution to select suitable features (the embedded approach) Filter and wrapper approaches attributes Subset selection One subset evaluation Stopping criterion Selected subset Validate with Mining task okNot ok

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Data dimension reduction –What: reduce redundancy implied among attributes e.g. are all 9600 dimensions for a 120x80 pixel image necessary? –Curse of dimensions: as dimensionality increases Data become more diverse, and any patterns are getting less significant and more peculiar. The processing time may increase substantially. –Why: to reduce redundancy and effects of the curse –How: Linear algebra techniques –Principal component analysis (PCA) –Independent component analysis (ICA) –Single value decomposition (SVD) Feature selection (as described before)

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Feature creation –What: to create a new set of features from the original features –Purpose: in the new feature space, meaningful and relevant patterns can be extracted more easily. The number of features may be reduced. –How: Using feature extraction methods to extract new features from the existing ones, e.g. extracting colour, texture and shape from image of pixel values Mapping data to a new space, e.g. wavelet transformation of pixel values of images to a frequency domain Constructing new features from the existing ones using domain knowledge, e.g. using transaction dates to construct a new feature customer tenure that indicates the loyalty of the customer to the company

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Data discretisation –What: to convert continuous attribute values to discrete categorical values –The purposes: Requirement for some data mining solutions Better data mining results (not always) –How: 1.Deciding how many categories to have and where split points should be 2.Mapping values to categories Determine the number & locations of the split points Mapping values within each sub-range to a category label t1t1 t2t2 t3t3 t4t4

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Data discretisation (cont ’ d) –Discretisation methods: Unsupervised: without concern to the outcome of a specific attribute, normally used for clustering and association rule mining e.g. equal width, equal depth, clustering Supervised: with respect to the outcome of the class attribute, normally used for classification –Simple methods: sorting according to the class attribute, and then discretising the attribute values for each class. –Sophisticated methods: the discretisation of the attribute values purifies the outcome of the class, e.g. using entropy to measure the degree of purity, and deciding the split points recursively, similar to decision tree induction –Merging methods, merging small intervals into a larger one with a stop criterion

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Data binarisation –What: to convert discrete categorical values to binary Boolean attribute values –The purpose: the same as for discretisation –How: Convert m categorical values to values in [0, m-1] Convert each to binary number of n bits where n = log 2 m Use m asymmetric binary variables to represent each of m values

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Variable transformation –What: transform all values of an attribute to other values –The purposes: Remove the effect of the outlier values Make the result data visualisation more interpretable Make the values more comparable –How: Transformation using function e.g. log(x) Standardisation/normalisation e.g. division-by-range

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Pre-processing Handling missing values –What: to treat attributes with null values –The purposes: Improve data quality Better mining results –How: Elimination (may not always be possible) Using sensible default, e.g. Spending Amount is set to 0 By data imputation –Average, median, or mode of the whole data population –Average, median or mode of the nearest neighbours Postponing the handling and making the mining methods adaptive to missing values

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration Exploring data before mining –Knowing data is essential for successful data mining –Purposes: Better understanding of the characteristics of data Better decision over data pre-processing tasks Even being able to discover some hidden patterns –Categories of data exploration techniques Summary statistics: using a small set of descriptors to describe the characteristics of a large data set Data visualisation: using graphical or tabular forms to reveal hidden data patterns Online Analytic Processing (OLAP) –Data exploration and exploratory data analysis (EDA)

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration Summary statistics –Frequency and mode for categorical attributes: Frequency of value Mode: the most frequently occurred value –Percentiles for ordinal or continuous attributes: Given an attribute x and an integer p (0 p 100), the percentile x p is a value of x such that p% observed values of x are less than x p. –Mean and median for continuous attributes: Mean and median Median is a better indication of “ average ” when data distribution is skewed or outliers are present –Trimmed mean and median (after trimming top and bottom p%)

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration Summary statistics (cont ’ d) –Measures of spread: Range Variance ( 2 ) Standard Deviation ( ) Absolute average deviation (AAD) –Multivariate summary statistics Mean vector Matrix of covariance Correlation

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration Data visualisation –Rationale: human eyes are good at spotting patterns, particularly visual patterns. –Major ways of visualising data Tabular form Graphical form Points and links –Visual representation must be related to the data types of the attributes –Visualising data as well as all its implicit relationships –The visualisation must be comprehensible –The visualisation of data must tell the truth

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration Data visualisation techniques Pie Chart Bar Chart Stem & Leaf Plot Scatter Plot Parallel Dimension Chart Star Dimension Chart

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration Online analytic processing (OLAP) –Interactive reporting tool –Treating a data set as a multidimensional hypercube –Fast operation and fast result delivery –A typical OLAP query: “For each product, find its market share in its category today minus its market share in its category in 1994” –Result of the OLAP query:

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration OLAP: Multidimensional hypercube JanFebDecMarch Buckingham Milton Keynes Northampton 1998 2000 1999 Total Customer = 5 Customer Names March Milton Keynes 1999

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning OLAP: Hierarchies winterspringsummer Buckingham Milton Keynes Northampton 1998 2000 1999 autumn Data Exploration JanuaryFebruaryMarch winter April May June spring JulyAugustSeptember summer OctoberNovemberDecember autumn JanFebDecMarch Buckingham Milton Keynes Northampton 1998 2000 1999

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration OLAP: Operations –Pivoting Selecting attributes to define the cube Visually rotating the cube to show a face –Slicing and dicing Selecting a part of a cube Visually slicing a segment of a cube along a dimension –Rolling-up Moving up along a hierarchy –Drilling-down Moving down along a hierarchy –Performing aggregate functions while rolling-up or drilling-down

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration in Weka Explorer ARFF file format Schema section Data section Data set name Categorical attribute name and values Numeric attribute names and types One data record per line; Values separated by “,”; “?” represents unknown.

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration in Weka Explorer Glance of an opened data set Summary statistics Visualisation of value distribution

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration in Weka Explorer Visualisation in Weka (limited)

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Data Exploration in Weka Explorer Filters for pre-processing –Many filters –Supervised/unsupervised –Attribute/instance –Choose followed by parameter setting in command line

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Chapter Summary The domain types determine the validity of operations applied. Transformation from one domain to another must preserve the domain characteristics. Data sets can be of various forms and from different sources. Data warehouse serves as a data source for data mining. Data quality is relevant to the intended application purpose. Data pre-processing operations are essential for good mining. Knowing the data is important for good data mining. Understanding of data is achieved via exploring, summarising and visualising data. OLAP serves as a data exploration and summarisation tool.

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Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning References Read Chapter 3 of Data Mining Techniques and Application Useful further references Tan, P-N., Steinbach, M. and Kumar, V. (2006), Introduction to Data Mining, Addison-Wesley, Chapters 2 and 3

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