Data Mining and Analytics: AIM411 Course Outline PDF

Data Mining and Analytics: AIM411 Introduction to Data Mining 1 Teaching Staff Lecturer: Dr. Ahmed Abdelhafeez (6201) Monday 2:25 PM to 3:55 PM Lab: Eng. Shady Ahmed Bedeir (6203) Thursday Sec (1) 10:20 AM to 11:50 AM  Cou...

Data Mining and Analytics: AIM411 Introduction to Data Mining 1 Teaching Staff Lecturer: Dr. Ahmed Abdelhafeez (6201) Monday 2:25 PM to 3:55 PM Lab: Eng. Shady Ahmed Bedeir (6203) Thursday Sec (1) 10:20 AM to 11:50 AM  Course Assessment ▪ Total Marks: 100 marks ✓ Final Exam: 40 marks ✓ Practical Exam: 20 marks ✓ Midterm: 20 marks ✓ Class work: 20 marks (2 Quizzes + Project) Google Classroom Sec (1) https://classroom.google.com/c/NzIwOTU0NTQ5NTcy Classroom code: 4t46lsf Exams Quiz 1 (21, October 2024) 5 degrees Quiz 2(25, November 2024) 5 degrees Project 28 October 10 degrees  Course Staff : Instructor Dr. Ahmed Abdel Hafeez Ahmed Abdelhafeez Ibrahim was born in Egypt, on September 1st, 1973. He received his B.Sc. from Military Technical College 1996. M.Sc. degree in Computer Engineering from the Faculty of Engineering, Arab Academy for Science and Technology and Maritime Transport 2017. PhD from the Faculty of Engineering, Ain Shams University 2023. His research interests in employing AI & Machine Learning techniques, deep learning, ensemble learning, image processing (mostly medical), pattern recognition, Data Science, and neutrosophic techniques. He is currently an Assistant Professor researcher at the Department of Artificial Intelligence at October 6th University.  Course Staff : Instructor He has an h-index of 10 on Google Scholar. He is a managing editor for SciNexus Journal, a multidisciplinary journal. He has 60 research papers. A reviewer for thirty research papers in five ranked journals He is an author for Nehdet Misr Publishing Group. He is a lecturer in Elforqan training in Qatar. Part-time Lecturer at the Faculty of Computer Science, Arab Academy. ICDL, IC3, Master of Microsoft Office, CCNA, ISO, Huawei HCIA certified in 5G and AI and Routing and Switching, Huawei Instructor, IBM certified in Big Data and AI, TOEFL grade 578. He has thirty presentations on the SlideShare website. Course Outline Data Preprocessing Measuring Data Similarity and Dissimilarity Clustering Algorithms and applications Partitioning methods Hierarchical methods Density-based methods Mining Frequent Patterns Associations and Correlations Pattern Evaluation Outlier detection Web Mining. Large-scale Data is Everywhere! ▪ There has been enormous data growth in both commercial and scientific databases due to advances in data generation and collection technologies E-Commerce Cyber Security ▪ New mantra ▪ Gather whatever data you can whenever and wherever possible. ▪ Expectations ▪ Gathered data will have value Social Networking: Twitter Traffic Patterns either for the purpose collected or for a purpose not envisioned. Sensor Networks Computational Simulations 9 Why Data Mining? Commercial Viewpoint Lots of data is being collected and warehoused – Web data ◆Googlehas Peta Bytes of web data ◆Facebook has billions of active users – purchases at department/ grocery stores, e-commerce ◆ Amazon handles millions of visits/day – Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong – Provide better, customized services for an edge (e.g. in Customer Relationship Management) 10 Why Data Mining? Scientific Viewpoint Data collected and stored at enormous speeds – remote sensors on a satellite ◆ NASA EOSDIS archives over petabytes of earth science data / year fMRI Data from Brain Sky Survey Data – telescopes scanning the skies ◆ Sky survey data – High-throughput biological data – scientific simulations ◆ terabytes of data generated in a few hours Gene Expression Data Data mining helps scientists – in automated analysis of massive datasets – In hypothesis formation Surface Temperature of Earth 11 Great opportunities to improve productivity in all walks of life 12 Great Opportunities to Solve Society’s Major Problems Improving health care and reducing costs Predicting the impact of climate change Reducing hunger and poverty by Finding alternative/ green energy sources increasing agriculture production 13 What is Data Mining? Many Definitions – Non-trivial extraction of implicit, previously unknown and potentially useful information from data – Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns 14 Origins of Data Mining Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems Traditional techniques may be unsuitable due to data that is – Large-scale – High dimensional – Heterogeneous – Complex – Distributed A key component of the emerging field of data science and data- driven discovery 15 Data Mining Tasks Prediction Methods – Use some variables to predict unknown or future values of other variables. Description Methods – Find human-interpretable patterns that describe the data. 16  Data Mining Tasks … Data Tid Refund Marital Taxable Status Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 11 No Married 60K No 12 Yes Divorced 220K No 13 No Single 85K Yes 14 No Married 75K No 15 No Single 90K Yes 10 Milk 17  Predictive Modeling: Classification Find a model for class attribute as a function of the values of other attributes Model for predicting credit worthiness Class Employed # years at Level of Credit Yes Tid Employed present No Education Worthy address 1 Yes Graduate 5 Yes 2 Yes High School 2 No No Education 3 No Undergrad 1 No { High school, 4 Yes High School 10 Yes Graduate Undergrad } 10 … … … … … Number of Number of years years > 3 yr < 3 yr > 7 yrs < 7 yrs Yes No Yes No 18  Classification Example # years at Level of Credit Tid Employed present Education Worthy address 1 Yes Undergrad 7 ? # years at 2 No Graduate 3 ? Level of Credit Tid Employed present 3 Yes High School 2 ? Education Worthy address … … … … … 1 Yes Graduate 5 Yes 10 2 Yes High School 2 No 3 No Undergrad 1 No 4 Yes High School 10 Yes 10 … … … … … Test Set Training Learn Model Set Classifier 19 Examples of Classification Task Classifying credit card transactions as legitimate or fraudulent Classifying land covers (water bodies, urban areas, forests, etc.) using satellite data Categorizing news stories as finance, weather, entertainment, sports, etc Identifying intruders in the cyberspace Predicting tumor cells as benign or malignant Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil 20 Classification: Application 1 Fraud Detection – Goal: Predict fraudulent cases in credit card transactions. – Approach: ◆ Use credit card transactions and the information on its account-holder as attributes. – When does a customer buy, what does he buy, how often he pays on time, etc ◆ Label past transactions as fraud or fair transactions. This forms the class attribute. ◆ Learn a model for the class of the transactions. ◆ Use this model to detect fraud by observing credit card transactions on an account. 21 Classification: Application 2 Churn prediction for telephone customers – Goal: To predict whether a customer is likely to be lost to a competitor. – Approach: ◆ Use detailed record of transactions with each of the past and present customers, to find attributes. – How often the customer calls, where he calls, what time- of-the day he calls most, his financial status, marital status, etc. ◆ Label the customers as loyal or disloyal. ◆ Find a model for loyalty. 22 Classification: Application 3 Sky Survey Cataloging – Goal: To predict class (star or galaxy) of sky objects, especially visually faint ones, based on the telescopic survey images (from Palomar Observatory). – 3000 images with 23,040 x 23,040 pixels per image. – Approach: ◆ Segment the image. ◆ Measure image attributes (features) - 40 of them per object. ◆ Model the class based on these features. ◆ Success Story: Could find 16 new high red-shift quasars, some of the farthest objects that are difficult to find! From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 23 Classifying Galaxies Courtesy: http://aps.umn.edu Early Class: Attributes: Stages of Formation Image features, Characteristics of light waves received, etc. Intermediate Late Data Size: 72 million stars, 20 million galaxies Object Catalog: 9 GB Image Database: 150 GB 24 Regression Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. Extensively studied in statistics, neural network fields. Examples: – Predicting sales amounts of new product based on advetising expenditure. – Predicting wind velocities as a function of temperature, humidity, air pressure, etc. – Time series prediction of stock market indices. 25 Clustering Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups Inter-cluster Intra-cluster distances are distances are maximized minimized 26  Applications of Cluster Analysis Understanding – Custom profiling for targeted marketing – Group related documents for browsing – Group genes and proteins that have similar functionality – Group stocks with similar price fluctuations Summarization – Reduce the size of large data sets Courtesy: Michael Eisen Clusters for Raw SST and Raw NPP 90 Use of K-means to 60 Land Cluster 2 partition Sea Surface 30 Temperature (SST) and Land Cluster 1 Net Primary Production latitude 0 (NPP) into clusters that Ice or No NPP -30 reflect the Northern Sea Cluster 2 and Southern -60 Hemispheres. Sea Cluster 1 -90 -180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180 Cluster 27 longitude Clustering: Application 1 Market Segmentation: – Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach: ◆ Collect different attributes of customers based on their geographical and lifestyle related information. ◆ Find clusters of similar customers. ◆ Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters. 28 Clustering: Application 2 Document Clustering: – Goal: To find groups of documents that are similar to each other based on the important terms appearing in them. – Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. Enron email dataset 29 Association Rule Discovery: Definition Given a set of records each of which contain some number of items from a given collection – Produce dependency rules which will predict occurrence of an item based on occurrences of other items. TID Items 1 Bread, Coke, Milk Rules Discovered: 2 Beer, Bread {Milk} --> {Coke} 3 Beer, Coke, Diaper, Milk {Diaper, Milk} --> {Beer} 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk 30 Association Analysis: Applications Market-basket analysis – Rules are used for sales promotion, shelf management, and inventory management Telecommunication alarm diagnosis – Rules are used to find combination of alarms that occur together frequently in the same time period Medical Informatics – Rules are used to find combination of patient symptoms and test results associated with certain diseases 31 Association Analysis: Applications An Example Subspace Differential Coexpression Pattern from lung cancer dataset Three lung cancer datasets [Bhattacharjee et a 2001], [Stearman et al. 2005], [Su et al. 2007] Enriched with the TNF/NFB signaling pathway which is well-known to be related to lung cancer P-value: 1.4*10-5 (6/10 overlap with the pathway) 32 Deviation/Anomaly/Change Detection Detect significant deviations from normal behavior Applications: – Credit Card Fraud Detection – Network Intrusion Detection – Identify anomalous behavior from sensor networks for monitoring and surveillance. – Detecting changes in the global forest cover. 33 Motivating Challenges Scalability High Dimensionality Heterogeneous and Complex Data Data Ownership and Distribution Non-traditional Analysis 34