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Data Mining Lecture 2: DBMS, DW, OLAP, and Data Preprocessing Contrasting Database and File Systems An Example of a Simple Relational Database The Relational Schema for the SaleCo Database The Entity Relationship Model The Development of Data Models The Relational Schema for the TinyCollege Database The Database System Environment Data Warehouse Data Life Cycle Process Continued The result - generating knowledge Methods for Collecting Raw Data The task of data collection is fairly complex. Which can create data-quality problem requiring validation and cleansing of data. • Collection can take place – in the field – from individuals – via manually methods • • • • time studies Surveys Observations contributions from experts – using instruments and sensors – Transaction processing systems (TPS) – via electronic transfer – from a web site (Clickstream) The Need for Data Analysis • Managers must be able to track daily transactions to evaluate how the business is performing • By tapping into the operational database, management can develop strategies to meet organizational goals • Data analysis can provide information about short-term tactical evaluations and strategies Transforming Operational Data Into Decision Support Data The Data Warehouse A data warehouse is a repository of subject-oriented historical data that is organized to be accessible in a form readily acceptable for analytical processing activities (such as data mining, decision support, querying, and other applications). • Benefits of a data warehouse are: – The ability to reach data quickly, since they are located in one place – The ability to reach data easily and frequently by end users with Web browsers. The Data Warehouse Continued • Characteristics of data warehousing are: – Time variant. The data are kept for many years so they can be used for trends, forecasting, and comparisons over time. – Nonvolatile. Once entered into the warehouse, data are not updated. – Relational. Typically the data warehouse uses a relational structure. – Client/server. The data warehouse uses the client/server architecture mainly to provide the end user an easy access to its data. – Web-based. Data warehouses are designed to provide an efficient computing environment for Web-based applications The Data Warehouse Continued Conceptual Modeling of Data Warehouses • Modeling data warehouses: dimensions & measures – Star schema: A fact table in the middle connected to a set of dimension tables – Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake – Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation Example of Star Schema time item time_key day day_of_the_week month quarter year Sales Fact Table time_key item_key branch_key branch location_key branch_key branch_name branch_type units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type location location_key street city province_or_street country time Example of Snowflake Schema time_key day day_of_the_week month quarter year item Sales Fact Table time_key item_key branch_key branch location_key branch_key branch_name branch_type units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_key supplier supplier_key supplier_type location location_key street city_key city city_key city province_or_street country Example of Fact Constellation time time_key day day_of_the_week month quarter year item Sales Fact Table time_key item_key item_name brand type supplier_type item_key location_key branch_key branch_name branch_type units_sold dollars_sold avg_sales Measures time_key item_key shipper_key from_location branch_key branch Shipping Fact Table location to_location location_key street city province_or_street country dollars_cost units_shipped shipper shipper_key shipper_name location_key shipper_type The Data Cube Multidimensional databases (sometimes called OLAP) are specialized data stores that organize facts by dimensions, such as geographical region, product line, salesperson, time. The data in these databases are usually preprocessed and stored in data cubes. • One intersection might be the quantities of a product sold by specific retail locations during certain time periods. • Another matrix might be Sales volume by department, by day, by month, by year for a specific region • Cubes provide faster: – – – – Queries Slices and Dices of the information Rollups Drill Downs Three-Dimensional View of Sales Cube: A Lattice of Cuboids all time time,item 0-D(apex) cuboid item time,location location item,location time,supplier time,item,location supplier 1-D cuboids location,supplier 2-D cuboids item,supplier time,location,supplier 3-D cuboids time,item,supplier item,location,supplier 4-D(base) cuboid time, item, location, supplier Operational vs. Multidimensional View of Sales Creating a Data Warehouse OLTP and OLAP Transactional vs. Analytical Data Processing Transactional processing takes place in operational systems (TPS) that provide the organization with the capability to perform business transactions and produce transaction reports. The data are organized mainly in a hierarchical structure and are centrally processed. This is done primarily for fast and efficient processing of routine, repetitive data. A supplementary activity to transaction processing is called analytical processing, which involves the analysis of accumulated data. Analytical processing, sometimes referred to as business intelligence, includes data mining, decision support systems (DSS), querying, and other analysis activities. These analyses place strategic information in the hands of decision makers to enhance productivity and make better decisions, leading to greater competitive advantage. OLTP vs. OLAP OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date detailed, flat relational isolated repetitive historical, summarized, multidimensional integrated, consolidated ad-hoc lots of scans unit of work read/write index/hash on prim. key short, simple transaction # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response usage access complex query OLAP Client/Server Architecture OLAP Server Arrangement OLAP Server with Multidimensional Data Store Arrangement OLAP Server With Local Mini Data Marts Data Mining: Extraction of Knowledge From Data Review: Data-Mining Phases Data Preprocessing Data Preprocessing • Why preprocess the data? • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation Why Data Preprocessing? • Data in the real world is a mess – incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data – noisy: containing errors or outliers – inconsistent: containing discrepancies in codes or names • No quality data, no quality mining results – Quality decisions must be based on quality data – Data warehouse needs consistent integration of quality data Cont’d • Just as manufacturing and refining are about transformation of raw materials into finished products, so too with data to be used for data mining • ECTL – extraction, clean, transform, load – is the process/methodology for preparing data for data mining • The goal: ideal DM environment Data Types • Variable Measures – – – – Categorical variables (e.g., CA, AZ, UT…) Ordered variables (e.g., course grades) Interval variables (e.g., temperatures) True numeric variables (e.g., money) • Dates & Times • Fixed-Length Character Strings (e.g., Zip Codes) • IDs and Keys – used for linkage to other data in other tables • Names (e.g., Company Names) • Addresses • Free Text (e.g., annotations, comments, memos, email) • Binary Data (e.g., audio, images) Multi-Dimensional Measure of Data Quality • A well-accepted multidimensional view: – – – – – – – – Accuracy Completeness Consistency Timeliness Believability Value added Interpretability Accessibility • Broad categories: – intrinsic, contextual, representational, and accessibility. Major Tasks in Data Preprocessing • Data cleaning – Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies • Data integration – Integration of multiple databases, data cubes, or files • Data transformation – Normalization and aggregation • Data reduction – Obtains reduced representation in volume but produces the same or similar analytical results • Data discretization – Part of data reduction but with particular importance, especially for numerical data Forms of data preprocessing What the Data Should Look Like • All data mining algorithms want their input in tabular form – rows & columns as in a spreadsheet or database table i.e. Give a sample file of SPSS What the Data Should Look Like • Customer Signature – Continuous “snapshot” of customer behavior Each row represents the customer and whatever might be useful for data mining What the Data Should Look Like • The columns – Contain data that describe aspects of the customer (e.g., sales $ and quantity for each of product A, B, C) – Contain the results of calculations referred to as derived variables (e.g., total sales $) What the Data Should Look Like 1. 2. 3. 4. Columns with One Value - Often not very useful Columns with Almost Only One Value Columns with Unique Values Columns Correlated with Target Variable (synonyms with the target variable) 1. 2. 3. Data Cleaning • Data cleaning tasks – Fill in missing values – Identify outliers and smooth out noisy data – Correct inconsistent data Missing Data • Data is not always available – E.g., many tuples have no recorded value for several attributes, such as customer income in sales data • Missing data may be due to – equipment malfunction – inconsistent with other recorded data and thus deleted – data not entered due to misunderstanding – certain data may not be considered important at the time of entry – not register history or changes of the data • Missing data may need to be inferred. How to Handle Missing Data? • Ignore the tuple: usually done when class label is missing (assuming the tasks in classification—not effective when the percentage of missing values per attribute varies considerably. • Fill in the missing value manually: tedious + infeasible? • Use a global constant to fill in the missing value: e.g., “unknown”, a new class?! • Use the attribute mean to fill in the missing value • Use the attribute mean for all samples belonging to the same class to fill in the missing value: smarter • Use the most probable value to fill in the missing value: inference-based such as Bayesian formula or decision tree Noisy Data • Noise: random error or variance in a measured variable • Incorrect attribute values may due to – – – – – faulty data collection instruments data entry problems data transmission problems technology limitation inconsistency in naming convention • Other data problems which requires data cleaning – duplicate records – incomplete data – inconsistent data How to Handle Noisy Data? • Binning method: – first sort data and partition into (equi-depth) bins – then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc. • Clustering – detect and remove outliers • Combined computer and human inspection – detect suspicious values and check by human • Regression – smooth by fitting the data into regression functions Simple Discretization Methods: Binning • Equal-width (distance) partitioning: – It divides the range into N intervals of equal size: uniform grid – if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B-A)/N. – The most straightforward – But outliers may dominate presentation – Skewed data is not handled well. • Equal-depth (frequency) partitioning: – It divides the range into N intervals, each containing approximately same number of samples – Good data scaling – Managing categorical attributes can be tricky. Binning Methods for Data Smoothing * Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 * Partition into (equi-depth) bins: - Bin 1: 4, 8, 9, 15 - Bin 2: 21, 21, 24, 25 - Bin 3: 26, 28, 29, 34 * Smoothing by bin means: - Bin 1: 9, 9, 9, 9 - Bin 2: 23, 23, 23, 23 - Bin 3: 29, 29, 29, 29 * Smoothing by bin boundaries: - Bin 1: 4, 4, 4, 15 - Bin 2: 21, 21, 25, 25 - Bin 3: 26, 26, 26, 34 Cluster Analysis Regression y Y1 Y1’ y=x+1 X1 x Data Integration • Data integration: – combines data from multiple sources into a coherent store • Schema integration – integrate metadata from different sources – Entity identification problem: identify real world entities from multiple data sources, e.g., A.cust-id B.cust-# • Detecting and resolving data value conflicts – for the same real world entity, attribute values from different sources are different – possible reasons: different representations, different scales, e.g., metric vs. British units Handling Redundant Data in Data Integration • Redundant data occur often when integration of multiple databases – The same attribute may have different names in different databases – One attribute may be a “derived” attribute in another table, e.g., annual revenue • Redundant data may be able to be detected by correlational analysis • Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality Data Transformation • Smoothing: remove noise from data • Aggregation: summarization, data cube construction • Generalization: concept hierarchy climbing • Normalization: scaled to fall within a small, specified range – min-max normalization – z-score normalization – normalization by decimal scaling • Attribute/feature construction – New attributes constructed from the given ones Data Transformation: Normalization • min-max normalization v minA v' (new _ maxA new _ minA) new _ minA maxA minA • z-score normalization v meanA v' stand _ devA • normalization by decimal scaling v v' j 10 Where j is the smallest integer such that Max(| v ' |)<1 Principal Component Analysis • Given N data vectors from k-dimensions, find c <= k orthogonal vectors that can be best used to represent data – The original data set is reduced to one consisting of N data vectors on c principal components (reduced dimensions) • Each data vector is a linear combination of the c principal component vectors • Works for numeric data only • Used when the number of dimensions is large Principal Component Analysis X2 Y1 Y2 X1 Regression and Log-Linear Models • Linear regression: Data are modeled to fit a straight line – Often uses the least-square method to fit the line • Multiple regression: allows a response variable Y to be modeled as a linear function of multidimensional feature vector • Log-linear model: approximates discrete multidimensional probability distributions Regress Analysis and Log-Linear Models • Linear regression: Y = + X – Two parameters , and specify the line and are to be estimated by using the data at hand. – using the least squares criterion to the known values of Y1, Y2, …, X1, X2, …. • Multiple regression: Y = b0 + b1 X1 + b2 X2. – Many nonlinear functions can be transformed into the above. • Log-linear models: – The multi-way table of joint probabilities is approximated by a product of lower-order tables. – Probability: p(a, b, c, d) = ab acad bcd Sampling • Allow a mining algorithm to run in complexity that is potentially sub-linear to the size of the data • Choose a representative subset of the data – Simple random sampling may have very poor performance in the presence of skew • Develop adaptive sampling methods – Stratified sampling: • Approximate the percentage of each class (or subpopulation of interest) in the overall database • Used in conjunction with skewed data • Sampling may not reduce database I/Os (page at a time). Sampling Raw Data Sampling Raw Data Cluster/Stratified Sample References • Design and Implementation of Database Systems (2005), Rob • Michael J. A. Berry and Gordon S. Linoff (2004), Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, 2nd ed., Wiley • Introduction to Data Mining and Knowledge Discovery, Third Edition, ISBN: 1-892095-02-5 (Can be downloaded via website for free) • Tan, P., Steinbach, M., and Kumar, V. (2006) Introduction to Data Mining, 1st edition, AddisonWesley, ISBN: 0-321-32136-7. • Vasant Dhar and Roger Stein, Prentice-Hall (1997), Seven Methods for Transforming Corporate Data Into Business Intelligence • H. Witten and E. Frank (2005), Data Mining:Practical Machine Learning Tools and Techniques, 2nd edition, Morgan Kaufmann, ISBN: 0-12-088407-0, closely tied to the WEKA software. • Ethem ALPAYDIN, Introduction to Machine Learning, The MIT Press, October 2004, ISBN 0-26201211-1 • J. Han and M. Kamber (2000) Data Mining: Concepts and Techniques, Morgan Kaufmann. Database oriente.