Statistics and Data Mining

Statistics and Data Mining A B M Shawkat Ali PowerPoint permissions Cengage Learning Australia hereby permits the usage and posting of our copyright controlled PowerPoint slide content for all courses wherein the associated text has been adopted. PowerPoint slides may be placed on course management systems that operate under a controlled environment (accessed restricted to enrolled students, instructors and content administrators). Cengage Learning Australia does not require a copyright clearance form for the usage of PowerPoint slides as outlined above. Copyright 2007 Cengage Learning Australia Pty Limited 1

Objectives Objectives On completion of this lecture you should know: What is Data Mining and how does it related with Statistics? The basic ramifications of Data Mining KDD, Data Query and Data Mining Basic understanding of PDCA cycle Current applications of Data Mining 2

Data mining: A definition Ask yourself: What is gold mining? 3

Data mining (DM) The process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data- (Roiger and Geatz, 2003). Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data using machine learning, statistical and visualization techniques (Frawley et al., 1992). Many experts agree that data mining should not be automatic human intervention and interpretation is essential. 4

Knowledge discovery in databases (KDD) Data Mining (DM) is one step of the KDD process. DM is an information extraction process and KDD is making sense of the information. But now no distinction is made between the two. The application of the scientific method occurs in DM. 5

Steps of Data Mining

An example Example 1.1 A leading Australian supermarket chain employs a data mining expert to analyse local buying patterns. Analysis: When a customer buys honey on Friday or Sunday, they also usually buy bread. (cont.) 7

Observation: More people buy honey and bread together on Friday and Sunday. Business Benefit: The supermarket chain can use this information in various ways to increase revenue. For instance, they can move the bread shelf closer to the honey shelf and make sure that bread and honey are sold at full price during the weekend.

Example: Amazon.com purchase suggestion Amazon.com increased sales by 15%, using data/text mining generated purchase suggestions

Plan-Do-Check-Act (PDCA) cycle Figure 1.1 Plan-Do-Check-Act (PDCA) cycle of Scientific method 10

How Can We Do Data Mining?

Data mining lifecycle Problem identification Taking Action Collation of data Interpretation of the Discovered knowledge Act Plan Data preprocessing Choosing an algorithm Check Do Iteration Model construction and Evaluation Figure 1.2 KDD or data mining lifecycle in the framework of PDCA cycle. 12

Data mining and It s branches Statistics: The model is king (Hand) Data Mining: The data is king

Statistics vs. Data Mining: Concepts Feature Statistics Data Mining Type of Problem Well structured Unstructured / Semi-structured Inference Role Objective of the Analysis and Data Collection Size of data set Explicit inference plays great role in any analysis First objective formulation, and then - data collection Data set is small and hopefully homogeneous No explicit inference Data rarely collected for objective of the analysis/modeling Data set is large and data set is heterogeneous Paradigm/Approach Theory-based (deductive) Synergy of theory-based and heuristic-based approaches (inductive) Signal-to-Noise Ratio STNR > 3 0 < STNR <= 3 Type of Analysis Confirmative Explorative Number of variables Small Large

Statistics vs. Data Mining: Regression Modeling Feature Statistics Data Mining Number of inputs Small Large Type of inputs Multicollinearity Distributional assumptions, homoscedasticity, outliers, missing values Type of model Interval scaled and categorical with small number of categories (percentage of categorical variables is small) Wide range of degree of multicollinearity with intolerance to multicollinearity Intolerance to distributional assumption violation, homoscedasticity, Outliers/leverage points, missing values Linear / Non-linear / Parametric / Non- Parametric in low dimensional X- space (intolerance to uncharacterizable non-linearities) Any mixture of interval scaled, categorical, and text variables Severe multicollinearity is always there, tolerance to multicollinearity Tolerance to distributional assumption violation, outliers/leverage points, and missing values Non-linear and nonparametric in high dimensional X-space with tolerance to uncharacterizable non-linearities

Steps of DM: Problem identification The problem should be meaningful. We also need to set the level of expectation for the solution, say 80% or 98% satisfaction. Without business understanding and requirements, useful data mining cannot be done. 16

Collation of data: The problem definition provides us with the scope of relevant data. A data mining technique may require millions and often billions of cases of data. However, typically, a data mining technique is applied to a few hundred or a few thousand instances of data. 17

Data preprocessing: Is dependent on the source: If the data comes from a data warehouse, no preprocessing of data is usually required because the warehouse data has already been filtered, cleaned and missing values taken care of. For transactional data, it needs to be organised and cleaned such that a data mining technique can be readily applied. (cont.) 18

The data has to be made consistent across sources. For example, in one database male and female may be represented as M and F, and in another database it may be represented as 1 and 0. Such anomalies have to be removed and any representation has to be made uniform.

Algorithm selection: Now-a-days quite a good number of data mining algorithms are available for public use. In general, parametric algorithms are relatively more suited for the data mining task. This involves choosing the optimal parameters to receive the best solution. 20

Data processing: This may involve data normalisation, data transformation or data integration. Some algorithms cannot work with categorical data, some cannot work with numerical data, and yet, some others cannot work with either unless the values meet certain criteria. 21

Another important part of this task is data splitting, which is about deciding which part of data is to be used for model building (training data) and which part for model testing (test data). This step is identified as data preparation in CRISP-DM.

Model construction and evaluation: Model evaluation or testing is an important step for maximising the amount of information that can be extracted from the dataset. If we see the model performances to be unacceptable, we follow the iterative path of choosing a different data mining algorithm or having a different set of features from the dataset. 23

Discovering knowledge: Final stage of DM. Verify the quality of knowledge. If satisfied, go ahead for implementation. 24

Taking action: We may act based on the discovered knowledge, which could bring rewards. Taking action can simply mean applying the model to new instances. This step is identified as deployment in CRISP- DM. 25

Types of knowledge Shallow knowledge: It is simply what makes up a computers response. For example, we may learn that Australian Stock Exchange generally follows the lead of Wall Street, but we wouldn't necessarily know why. Deep knowledge: It is the underlying reason behind such relationships. For example, which gene is responsible for diabetics. 26

Steps of data mining for business Cross-Industry Standard Process for Data Mining (CRISP-DM): Business Understanding Data understanding Data Preparation Modelling Evaluation Deployment (cont.) 27

We identified 8 steps considering all possible applications of data mining including business sector. These 8 steps have been described within the framework of PDCA (Plan-Do-Check- Act) cycle highlighting the highly iterative aspect of the process. 28

Data query versus data mining Data Query A list of customers who used MasterCard to buy medicine from a pharmacy. A list of employees who will reach retiring age next year. A list of residents in a locality who became diabetic before reaching the age of 50. Find all customers who have purchased diapers. 29

Data Mining Develop a profile of MasterCard holders who will take advantage of the forthcoming sale promotion of the pharmacy. Develop a list of employees, who are likely to avail themselves of the voluntary early retirement scheme when they reach the retiring age. Construct some rules about the lifestyle of residents of a locality which may reduce the occurrence of diabetes at an early age. Find all items which are frequently purchased with diapers.

The learning process What is Learning? It s a process to gather knowledge. Four Levels of Learning: Facts - simple truths Concepts - relationships Procedures - algorithms Principles - all pervading truths 31

Types of learning Supervised Learning: Learning with the help of a supervisor Example 1.2 In a biomedical study, medical records for a set of healthy patients and a set of patients with heart disease have been collected. 32

The data mining technique to this study would be to learn what combination of attributes obesity, high-cholesterol, smoking habit, etc. characterises patients with heart disease and distinguishes them from healthy patients.

Types of learning (cont.) Table 1.1 Supervised learning data structure Obesity High- Cholesterol Smoker Class Patient 1 Yes Yes Yes Sick Patient m No No No Healthy 34

Types of learning (cont.) Unsupervised Learning Learning without a supervisor Example 1.3 A credit card company wants to promote credit card insurance. 35

Types of learning (cont.) Table 1.2 Unsupervised learning data structure Home Insurance Life insurance Income range Person 1 Yes Yes 50-60K Person m Yes No 40-50K 36

Reinforcement Learning Leaning from incidence Example 1.4 Some players have trouble arriving on time to the practice match. To lift the team spirit coach orders all the players to run 5 extra laps in the stadium. The coach claims that this application had to be given only once a year. 37

The history of data mining 1700-1939: First Generation of Data Mining. It was based on Statistics. 1940-1989: Second Generation of Data Mining. First introduction of Artificial Intelligence (AI) in Data Mining. 1960s: Data Mining starts the real journey. The late 1960s saw the introduction of clustering techniques (Unsupervised Learning ) in the field of Information Retrieval. 1990-onwards: Third Generation of Data Mining. People introduced better techniques by combining Statistics and AI. 38

Data mining strategies Classification Example 1.5 A bank wishes to determine the credit risk of a credit card applicant. The application is either approved or rejected. 39

Cont. Feature F1 Classification F2

Association Example 1.6 A leading supermarket chain had 100,000 point-of sale transactions last month. An association rule miner observes that 25,000 of these transactions include both banana and bread and 8,000 transactions include three items banana, bread and honey. 41

Cont.

Clustering Example 1.7 Clustering could be used by an insurance company to group important customers according to age, types of policies purchased, duration of membership, and prior claims history. 43

Cont.

Estimation Example 1.8 We are interested in estimating the blood sugar level of a new hospital patient. 45

Cont.

Novelty Detection Example 1.8 The heartbeat record of a healthy patient to an untrained eye is either plain noise or full of features or spikes. 47

Cont.

Sequence Detection Example 1.9 Thrombosis is a potential complication of collagen diseases. 49

Cont.

Popular data mining techniques Function Estimation-Based Algorithms: Neural Networks, Support Vector Machines etc. Lazy Learning-Based Algorithms: K-Nearest Neighbors, Lazy Bayesian Rules etc. Meta Learning-Based Algorithms: Adaboost, Bagging, and MetaCost etc. Probability-Based Algorithms: Naive Bayes, BayesNet etc. Tree-Based Algorithms: C4.5, Classification and Regression Tree (CART) and CHAID etc. 51

Neural Network

Support Vector Machine (SVM)

Decision Tree Outlook sunny rainy overcast humidity Yes windy high normal false true No Yes Yes No

Data mining applications Common with insurance agencies and banks. For example, Bank of America. Common in gambling industry. For example, Harrah s Entertainment Inc. Common with large businesses. For example, Wal-Mart. 55

Banking loan/credit card approval: Predict good customers based on old customer profiles. Customer relationship management (CRM): Identify those who are likely to leave for a competitor. Targeted marketing: Identify likely respondents to promotions. 56

Fraud detection telecommunications, financial transactions: Identify fraudulent transactions from an online stream of events. Manufacturing and production: Automatically adjust knobs when process parameter changes Medicine disease outcome, effectiveness of treatments: Analyse patient disease history: find relationship between disease and symptoms.

Molecular/Pharmaceutical: Identify new drugs. Scientific data analysis: Identify new galaxies by searching for sub clusters. Website/store design and promotion: Find preferences of website/store visitor and modify layout accordingly. 58

Challenges of data mining Size of dataset High dimensionality Over-fitting Missing and noisy data Rapidly changing data Mixed dataset Human intervention and interpretation 59

Future of data mining Credit risk assessment Customer relationship management Attrition of small business customers Early weather warning Stock price forecast Quick machinery fault detection Brain tumor prediction 60

These and other such issues are already seeing the introduction of data mining technology in their solution strategies. The long-term prospects are truly exciting. Data mining technology has already opened a new dimension in medical research. For example, a gene data analyst can tell us who has breast cancer and who does not.

Privacy in Data Mining Mining of public and government databases is done, though people have, and continue to raise concerns. Wiki quote: "data mining gives information that would not be available otherwise. It must be properly interpreted to be useful. When the data collected involves individual people, there are many questions concerning privacy, legality, and ethics."

Prevalence of Data Mining Your data is already being mined, whether you like it or not. Many web services require that you allow access to your information [for data mining] in order to use the service. Google mines email data in Gmail accounts to present account owners with ads. Facebook requires users to allow access to info from non Facebook pages. Facebook privacy policy: "We may use information about you that we collect from other sources, including but not limited to newspapers and Internet sources such as blogs, instant messaging services and other users of Facebook, to supplement your profile. This allows access to your blog RSS feed (rather innocuous), as well as information obtained through partner sites (worthy of concern).

Key learning outcomes What is Data Mining? The basic ramifications of Data mining KDD, Data Query and Data Mining Basic Understanding of PDCA cycle Current Applications of Data Mining 64