CS513-Data Mining. Lecture 2: Understanding the Data. Waheed Noor

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1 CS513-Data Mining Lecture 2: Understanding the Data Waheed Noor Computer Science and Information Technology, University of Balochistan, Quetta, Pakistan Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

2 Outline 1 Patterns Class Activity 2 Types of Learning 3 Model 4 Data Mining Algorithms 5 Understanding your Data: Input 6 Issues with Real World Data Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

3 Outline 1 Patterns Class Activity 2 Types of Learning 3 Model 4 Data Mining Algorithms 5 Understanding your Data: Input 6 Issues with Real World Data Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

4 Pattern Example Example Consider the data of contact lens prescription from an optician, the task is to prescribe a soft, hard or no contact lens to the patient based on his/her information. We will analyze past data in order to find some patterns, if possible. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

5 Contact Lens Data Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

6 Finding Patterns: Illustration if tear production rate = reduced then recommendation = none elseif age = young and astigmatic=no then recommendation=soft else recommendation = hard end if Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

7 What we get These pattern may not be enough to be generalized as rule, since example is a simple one and we do not have enough data. (i.e., may be incomplete). We can say this pattern just summarizes the data. How many possible values of input required for extracting useful patterns? ( ) Actually, the data mining task needs to generalize to new examples as well. Real life data often contains examples in which values of some features are noisy or missing. Which can effect the performance of data mining technique. Misclassification can even occur on the datasets that were used to train/learn the method. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

8 Outline 1 Patterns Class Activity 2 Types of Learning 3 Model 4 Data Mining Algorithms 5 Understanding your Data: Input 6 Issues with Real World Data Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

9 Weather Problem Example Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

10 Some Complexity: Numeric Attributes Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

11 Classification What we have seen so far are classification rules, i.e., classifying examples We can also look examples for rules that associate values of different attributes, Association Rules. Example if temperature = cool then humidity = normal if humidity = normal and windy = false then play = yes if windy = false and play = no then outlook = sunny and humidity = high Can you identify one? Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

12 Rules Definition (Rules) Set of conditions/decisions that can be specifically and implicitly interpreted in some order. They are helpful tools for making classification and association of examples. E.g., decision list, that is interpreted in a sequence, or decision tree, that are interpreted hierarchically. Sometime we may get a rule set that gives unique prescription for every conceivable example, such as for above examples However, it is generally not possible, there may be situation where no rule is applicable or more than one rules are applicable (i.e., conflict will rise then we go to probability or weigths) Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

13 Outline 1 Patterns Class Activity 2 Types of Learning 3 Model 4 Data Mining Algorithms 5 Understanding your Data: Input 6 Issues with Real World Data Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

14 Types of Learning in Data Mining Classification Learning: Learning is achieved by presenting classified examples (historical/training data) in order to classify unseen examples (future/test data). Association Learning: Association among features is learned from historical data. Here it is not just limited to learning for one particular attribute or feature. Clustering: Examples are grouped together based on some similarity or homogeneity. Numeric Prediction: The outcome to be predicted is not a discrete class but the prediction is made for numerical outcome. Definition (Concept) Any thing that is being learned is called the concept, and the output of the learning method is known as concept description. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

15 Outline 1 Patterns Class Activity 2 Types of Learning 3 Model 4 Data Mining Algorithms 5 Understanding your Data: Input 6 Issues with Real World Data Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

16 Model Vs. Pattern Definition (Model) Describe global summary of the dataset, i.e., makes statement about any point in full measurement space. For example, predicting a values or assigning an example to the cluster. Even if some points in this space is missing. Model Representation At its simplest form, a model can be represented by: Y = ax + c where Y and X are variables (Y is outcome), and a and c are model parameters. This is a linear model, since Y is a linear function of X a. a Unlike Statistics, linearity here is in terms of variables rather than model parameters Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

17 Pattern Definition Describes a structure relating to a small parts (local) of data or measurement space. For example, mail order purchase data may reveal a pattern that customers buying particular product also buy an other product. Example (Fraud Detection) Bank transaction data can be mined for fraud detection, once the usual behaviors are described by patterns. Once these structures are defined their parameters can be estimated from the data. Models or patterns with parameter values are called fitted models or patterns respectively. Fitted models or patterns are then used on future data. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

18 Outline 1 Patterns Class Activity 2 Types of Learning 3 Model 4 Data Mining Algorithms 5 Understanding your Data: Input 6 Issues with Real World Data Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

19 Why Algorithms We have seen that the data mining tasks rise in variety of different real world applications For example, Exploratory data analysis, descriptive modeling, predictive modeling, patterns and rules discovery, contents retrieval, and so on. To accomplish these tasks we need algorithms, termed as data mining algorithms Readings You should read about Real World Applications of Data Mining from different resources to build understanding of different types of problems and data mining tasks. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

20 Data Mining Algorithms Not very strict, generally there are four basic components of a data mining algorithm Components 1 Model or Pattern Structure: Describe the underlying structure or functional forms that we seek from the data. 2 Score Function: Also known as cost function, objective function or performance measure, It is used to evaluate or judge the learning capability and quality of the fitted structure (pattern or model). 3 Optimization or Searching: Optimizing the score function and searching through different possible model and pattern structures to find the best. 4 Data Management Strategy: Effective management of large data during optimization and searching. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

21 Outline 1 Patterns Class Activity 2 Types of Learning 3 Model 4 Data Mining Algorithms 5 Understanding your Data: Input 6 Issues with Real World Data Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

22 Understanding Input Definition (Example or Instance) A record or row in the data file is called an example or instance or observation. They may have relationship among them or independent of each other in some way. Definition (Attribute) The columns or fields of the data file that are fixed, predefined are known as features or attributes. An instance characterizes the set of attributes by its values. These attributes if selected or used for mining task then they will be referred as variables for the data mining algorithm. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

23 Types of Data Quantitative Data: Numerical data, either continuous (e.g., Amount of sales, temperature) or integer (e.g., number of students in a class) Qualitative Data: That approximates or characterizes but does not measure, e.g., present or absent, level of agreement. Categorical Data: That represents one of several (limited) categories, e.g., color of an object, gender of the customer etc. They are also some time called discrete as they represent some well separated categories. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

24 Measurement Levels Nominal : A variable can be treated as nominal when its values represent categories with no intrinsic ranking (for example, the departments of the company in which an employee works). Examples of nominal variables include region, zip code, and religious affiliation. Ordinal : A variable can be treated as ordinal when its values represent categories with some intrinsic ranking (for example, levels of service satisfaction from highly dissatisfied to highly satisfied). Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores. Scale : A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

25 Class Activity 1 Identify different types of data, and assign different measuring levels: Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

26 Class Activity 2 Identify different types of data, and assign different measuring levels: Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

27 Outline 1 Patterns Class Activity 2 Types of Learning 3 Model 4 Data Mining Algorithms 5 Understanding your Data: Input 6 Issues with Real World Data Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

28 Issues with Input Data Due to many reasons real world data is sometime inaccurate, inexact or incomplete as apposed to the assumption of data mining algorithms. Sparse Data Most attributes of the data may contain zero values, e.g., if a market basket data contains data of purchases by customers then for many products that customer has not purchased, quantity will be zero. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

29 Missing Values Respondent in a survey may refuse to answer few questions or malfunction instrument may not record data for some attributes or values of some attributes in some circumstances may not be measured. These dataset will then contain missing values for specific attributes. Missing Values may be represented in the dataset by an out-of-range value, or negative value if it is not possible for the attribute to have negative value, by a dash, question mark, etc. When collecting or recording data, one may not find an attribute useful for their operation but that attribute may be important for mining task, then we are faced with missing attributes. For example, university may not be interested in the parent s education or income but these attributed may have significance when mining students data for possible financial aid offer. Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

30 Example of Missing Values Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

31 Inaccurate Values Since data for data mining task is not explicitly collected or recorded for this purpose one should carefully analyze data for rogue attributes or attribute values. Inaccuracy may occur: Typography Measurement Error Merging data from different sources Deliberately Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32

32 References I Waheed Noor (CS&IT, UoB, Quetta) CS513-Data Mining March / 32