Welcome to CPS 5721 (Knowledge Discovery and Data Mining)

• SQL DML, aggregations, group by
• SQL DDL, upload CSV to MySQL
• Materialized view, data mart and data visualization
• Data mining concepts, case study
• Data warehousing, OLTP, OLAP
• Data processing: internet data, text and image
• Detect local minimum, local maximum, up and down trends
• Supervised vs unsupervised methods
• Binning
• Similarity, dissimilarity - distances, cosine, Manhattan (city block), euclidean, supernum
• Text mining and text similarity
• Web crawling, search engine
• Association, mining frequent pattern
• Classification, impurity, gain, decision tree
• Image classification - TensorFlow
• Regression, SVM
• Anomaly impact, outlier detection using Boxplot, regression, histogram, mean and standard deviation, Z-score,
• Clustering, K-means, hierarchical, dendrogram, 4-connected
• Statistical techniques
• Data visualization concepts
• Data mining trends, impacts
• Automated analytics - statistical functions, correlation, p-value
• Python Pandas, matplotlib, geopandas

• All the class materials (syllabus, slides, assignments, tools) are posted on the class Google drive. You must use the Kean email account to access the folder.
• You can access the latest syllabus here.
• This is a hands-on course. Students are required to bring a personal laptop to every class.
• Instructional Methods: lecture with slides, student presentations, class discussions and exercises, and project creations.
• Course grading: please see the course syllabus.
• In addition to the written exams, this combined course requires students to study and present a selected data mining research topics and implement 3 big homework on a Linux server.
• Grading policy: Total 1000 points.
  A: >=940, A-: 939-890, B+: 889-840, B: 839-800, B-: 799-760, C+: 759-720, C: 719-680, D: 679-640, F: below 640
  (C or better is needed for CS and IT majors.)
  (B- or better is needed for the CIS Graduate program.)
  

1. Summarize knowledge discovery and data mining (KDDM).
2. Summarize the historical perspective and the social impact of KDDM.
3. Compare and summarize the differences between KDDM and information access/retrieval.
4. Analyze KDDM processes, concepts, techniques, algorithms, and languages.
5. Demonstrate KDDM applications in business and the sciences.

• Textbook: Data Mining: Concepts and Techniques, 4th Edition, 2022, by Jiawei Han (Author), Jian Pei (Author), Hanghang Tong (Author), Publisher: Morgan Kaufmann, ISBN-13: 978-0128117606
• Python at W3Schools
• Anaconda for Python
• Twitter Developer
• Tweepy

• Students will need an account on obi.kean.edu (a Linux server). The instructor will create the accounts so students can do exercises and assignments from the first day of class.
• Students will need the following tools to connect to a Linux server. hostname: obi.kean.edu, port: 22, protocol: ssh
  Windows: Use "PuTTY" software to connect to the Linux server. You can download putty from http://www.putty.org/
  Mac: Use "terminal" software. Please refer to this tutorial.
• FileZilla-Client to transfer files between the Linux server and your desktop/laptop. You can download FileZilla-Client at https://filezilla-project.org/download.php?type=client
  The hostname and port # is the same as above. Protocol should be "sftp". The logon type should be "normal". You need to enter the user login and password.
• A good text editor: sublime, visual studeio code, or others. You can download sublime at https://www.sublimetext.com/3
• Create public_html and CPS5721 folders under your home directory on obi.kean.edu, and set proper permission for the folders and files. Please refer to this yoda web page procedures
• Practice these basic Unix commands: cd, ls -la, more, pwd, mkdir, rm, mv, cp, chmod, etc. Please refer to this Unix command document.
• Create and test your database account by following this procedure.

• HTML at W3Schools
• SQL
• PHP
• PHP MySQL functions
• You need to know the basic Unix commands.
• You can see more resources on the Code Samurai website.