1. Pattern Recognition Lecture 20: Data Mining 2 Dr. Richard Spillman Pacific Lutheran University

2. Class Topics Introduction Decision Functions Midterm One Midterm Two Data Mining Project Presentations Introduction Decision Functions Cluster Analysis Statistical Decision Theory Feature Selection Machine Learning Neural Nets

3. Review What is Data Mining? Data Mining Applications The Data Mining Process

4. Review - Problem We are data rich but information poor

5. Review – What is Data Mining? It is a method to get beyond the “tip of the iceberg” Data Mining/ Knowledge Discovery in Databases/ Data Archeology/ Data Dredging Information Available from a database

6. Review – Data Mining Issues DM is searching for strong patterns within big data that can be generalized to make predictions and to support future decisions... process DM is a process, not a product…. DM is a cooperative effort involving humans and computers... Features are identified from a problem domain and classification measured over many cases, to do classification or regression regression. Complexity is given by the number of cases, the number of features, and the number of distinct values that features can assume.

7. OUTLINE Data Mining Example Preprocessing Data Preprocessing Tasks

8. Example of Data Mining

9. Principle 137178766832525156430015 The sequence of numbers above is useless unless we "apply energy" to determine if there is any information in the sequence. Now suppose we are told to execute the following steps: 1. Re write the sequence as two-digit numbers. 2. Ignore any two-digit number less than 32 3. Substitute the ASCII characters for the two-digit numbers Data InformationKnowledgeWisdom

10. Result STEP 1: 13 71 78 76 68 32 52 51 56 43 00 15 STEP 2: 71 78 76 68 32 52 51 56 43 STEP 3: G O L D 4 3 8 + IF ( (Raising Gold) AND (> 500 Gold) ) THEN (Buy Gold) This “piece of knowledge,” which was probably articulated for us by a Wall Street Guru, could be coded as a rule. Now suppose we apply the following “2 cents of wisdom” to it:

11. Preprocessing Data

12. Why Preprocess Data? Data in the real world is dirty –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

13. View of Data Quality A well-accepted multidimensional view: –Accuracy –Completeness –Consistency –Timeliness –Believability –Value added –Interpretability –Accessibility

14. Preprocessing Tasks

15. Major Tasks 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

16. Data Cleaning Data cleaning tasks –Fill in missing values –Identify outliers and smooth out noisy data –Correct inconsistent data

17. 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.

18. What is to be done... 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

19. 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

20. What is to be done... 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

21. Bin Method 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

22. 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

23. Redundant Data 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

24. 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

25. Data Reduction Warehouse may store terabytes of data: Complex data analysis/mining may take a very long time to run on the complete data set Data reduction –Obtains a reduced representation of the data set that is much smaller in volume but yet produces the same (or almost the same) analytical results Data reduction strategies –Data cube aggregation –Dimensionality reduction –Numerosity reduction –Discretization and concept hierarchy generation

26. Discretization Three types of attributes: –Nominal — values from an unordered set –Ordinal — values from an ordered set –Continuous — real numbers Discretization: *divide the range of a continuous attribute into intervals –Some classification algorithms only accept categorical attributes. –Reduce data size by discretization –Prepare for further analysis

27. Summary of Preprocessing Data preparation is a big issue for both warehousing and mining Data preparation includes –Data cleaning and data integration –Data reduction and feature selection –Discretization A lot a methods have been developed but still an active area of research

28. Possible Quiz Why is preprocessing necessary? What can cause noisy data? What are the ways available to handle noisy data?