Data Engineering

1. Data Engineering

2. A Story of Data-Related Issues You receive an email from a medical researcher concerning a project that you are eager to work on. Hi, I've attached the data file that I mentioned in my previous email. Each line contains the information for a single patient and consists of five fields. We want to predict the last field using the other fields. I don't have time to provide any more information about the data since I'm going out of town for a couple of days, but hopefully that won't slow you down too much. Thanks and see you in a couple of days.

3. Continued… Despite some misgivings, you proceed to analyze the data. The first few rows of the file are as follows: Nothing looks strange. You put your doubts aside and start the analysis. Two days later you you arrive for the meeting, and while waiting for others to arrive, you strike up a conversation with a statistician who is working on the project.

4. Continued… Statistician: So, you got the data for all the patients? Data Miner: Yes. I haven't had much time for analysis, but I do have a few interesting results. Statistician: Amazing. There were so many data issues with this set of patients that I couldn't do much. Data Miner: Oh? I didn't hear about any possible problems. Statistician: Well, first there is field 5, the variable we want to predict. It's common knowledge among people who analyze this type of data that results are better if you work with the log of the values, but I didn't discover this until later. Was it mentioned to you? Data Miner: No. Statistician: But surely you heard about what happened to field 4? It's supposed to be measured on a scale from 1 to 10, with 0 indicating a missing value, but because of a data entry error, all 10's were changed into 0's. Data Miner: Interesting. Were there any other problems? Statistician: Yes, fields 2 and 3 are basically the same, but I assume that you probably noticed that. Data Miner: Yes, but these fields were only weak predictors of field 5.

5. Continued… Statistician: Anyway, given all those problems, I'm surprised you were able to accomplish anything. Data Miner: True, but my results are really quite good. Field 1 is a very strong predictor of field 5. I'm surprised that this wasn't noticed before. Statistician: What? Field 1 is just an identification number. Data Miner: Nonetheless, my results speak for themselves. Statistician: Oh, no! I just remembered. We assigned ID numbers after we sorted the records based on field 5. There is a strong connection, but it's meaningless. Sorry. Lesson: Get to know your data!

6. Formally: What is Data? • Collection of data objects and their attributes • An attribute is a property or characteristic of an object • Examples: eye color of a person, temperature, etc. • Attribute is also known as variable, field, characteristic, or feature • A collection of attributes describe an object • Object is also known as record, point, case, sample, entity, or instance Attributes Objects

7. Employee Age and ID Number • Two attributes of an employee are ID and age. • Both can be represented as integers. • However, while it is reasonable to talk about the average age of an employee, it makes no sense to talk about the average employee ID. • The only valid operation for employee IDs is to test whether they are equal. • There is no hint of this limitation, however, when integers are used to represent the employee ID attribute. • Knowing the type of an attribute is important because it tells us which properties of the measured values are consistent with the underlying properties of the attribute, and therefore, it allows us to avoid foolish actions, such as computing the average employee ID.

8. Types of Attributes • There are different types of attributes • Nominal • Examples: ID numbers, eye color, zip codes • Ordinal • Examples: rankings (e.g., taste of potato chips on a scale from 1-10), grades, height in {tall, medium, short} • Interval • Examples: calendar dates, temperatures in Celsius or Fahrenheit. • Ratio • Examples: temperature in Kelvin, length, time, counts

9. Properties of Attribute Values • The type of an attribute depends on which of the following properties it possesses: • Distinctness: =  • Order: < > • Addition: + - • Multiplication: * / • Nominal attribute: distinctness • Ordinal attribute: distinctness & order • Interval attribute: distinctness, order & addition • Ratio attribute: all 4 properties

10. Discrete and Continuous Attributes • Discrete Attribute • Has only a finite or countably infinite set of values • Examples: zip codes, counts, or the set of words in a collection of documents • Often represented as integer variables. • Note: binary attributes are a special case of discrete attributes • Continuous Attribute • Has real numbers as attribute values • Examples: 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. Typically, nominal and ordinal attributes are binary or discrete, while interval and ratio attributes are continuous. However, count attributes, which are discrete, are also ratio attributes.

11. Asymmetric Attributes • For asymmetric attributes, only presence -- a non-zero attribute value -- is regarded as important. • E.g. Transaction data • “Bread”, “Coke” etc are in fact (asymmetric) attributes and only their presence (i.e. value 1 or true) is important.

12. Types of data sets • Record • Data Matrix • Document Data • Transaction Data • Graph • World Wide Web • Molecular Structures • Ordered • Spatial Data • Temporal Data • Sequential Data • Genetic Sequence Data

13. Record Data • Data that consists of a collection of records, each of which consists of a fixed set of attributes

14. Document Data • Each document becomes a ‘term’ (word) vector, • each term is a component (attribute) of the vector, • the value of each component is the number of times the corresponding term occurs in the document.

15. Transaction Data • A special type of record data, where • each record (transaction) involves a set of items. • For example, consider a grocery store. The set of products purchased by a customer during one shopping trip constitute a transaction, while the individual products that were purchased are the items.

16. Data with Relationships among Objects • Examples: Generic graph and HTML Links Web search engines collect and process Web pages to extract their contents. It is well known, however, that the links to and from each page provide a great deal of information about the relevance of a Web page to a query, and thus, must also be taken into consideration.

17. Data with Objects That Are Graphs E.g. Chemical Data • Benzene Molecule: C6H6 Substructure mining: Which substructures occur frequently in a set of compounds? Ascertain whether the presence of any of these substructures is associated with the presence or absence of certain chemical properties, such as melting point or heat of formation.

18. Data Quality • What are the kinds of data quality problems? • How can we detect problems with the data? • What can we do about these problems? • Examples of data quality problems: • Data collection errors • Noise • Outliers • Missing values • Duplicate data We will study (2) and (3) in detail, later after the classification.

19. Outliers • Outliers are data objects with characteristics that are considerably different than most of the other data objects in the data set

20. Missing Values • Reasons for missing values • Information is not collected (e.g., people decline to give their age and weight) • Attributes may not be applicable to all cases (e.g., annual income is not applicable to children) • Handling missing values • Eliminate Data Objects • Estimate Missing Values • Ignore the Missing Value During Analysis • Replace with all possible values (weighted by their probabilities)

21. Data Preprocessing • Aggregation • Sampling • Dimensionality Reduction • Feature subset selection • Feature creation • Discretization and Binarization • Attribute Transformation

22. Aggregation • Sometimes "less is more" and this is the case with aggregation, the combining of two or more objects into a single object. • One way to aggregate transactions for this data set is to replace all the transactions of a single store with a single storewide transaction. • This reduces the number of data objects which is now equal to the number of stores. • How the values of each attribute are combined across all the records of a group (store for instance)? • Some quantitative attributes, e.g. price, are typically aggregated by taking a sum or an average. • Other attributes, e.g. item or date, are omitted.

23. Sampling • Sampling is the main technique employed for data selection. • It is often used for both the preliminary investigation of the data and the final data analysis. • Sampling is used in data mining because processing the entire set of data of interest is too expensive or time consuming. • The key principle for effective sampling is the following: • using a sample will work almost as well as using the entire data sets, if the sample is representative. • A sample is representative if it has approximately the same property (of interest) as the original set of data.

24. Sample Size 8000 points 2000 Points 500 Points

25. Curse of Dimensionality • When dimensionality increases, data becomes increasingly sparse in the space that it occupies • For classification. this can mean that there are not enough data objects to allow the creation of a model that reliably assigns a class to all possible objects. • Definitions of density and distance between points, which is critical for clustering and outlier detection, become less meaningful.

26. Feature Subset Selection • Redundant features • duplicate much or all of the information contained in one or more other attributes • Example: purchase price of a product and the amount of sales tax paid • Irrelevant features • contain no information that is useful for the data mining task at hand • Example: students' ID is often irrelevant to the task of predicting students' GPA

27. Feature Subset Selection • Techniques: • Brute-force approch: • Try all possible feature subsets as input to data mining algorithm • Embedded approaches: • Feature selection occurs naturally as part of the data mining algorithm • Filter approaches: • Features are selected before data mining algorithm is run

28. Discretization and Binarization • Some data mining algorithms, especially certain classification algorithms, require that the data be in the form of categorical attributes. • Algorithms that find association patterns require that the data be in the form of binary attributes. • Thus it is often necessary to transform a continuous attribute into a categorical attribute (discretization), and both continuous and discrete attributes may need to be transformed into one or more binary attributes (binarization).