++Proposal for Establishing
“PSU Professional Bi-Lingual Data & Text Mining Hub”
“Will Provide Guidance and Results for those who are Data-rich, yet Information-poor”
|
In the above box put a diagram that shows the servers and the clients structure in the Hub. |
|
|
Personnel with any of the following: domain , data base, and
statistical expertise such as: LINE-OF-BUSINESS EXECUTIVES AND FUNCTIONAL MANAGERS: Risk Managers, Customer Relationship Managers, Business Forecasters, Inventory Flow Analysts, Financial Forecasters, Direct Marketing Analysts, Medical Diagnostic Analysts, eCommerce Company Executives TECHNOLOGY PLANNERS: Who survey emerging technologies in order to prioritize corporate investment CONSULTANTS: Whose competitive environment is intensifying and whose success requires competency with data mining and related emerging information technologies RESEARCHERS: Who are interested in both structured bi-lingual data mining algorithms and their applications, as well as unstructured bi-lingual text mining algorithms.
All the Hub’s activities will provide learning opportunities for developing and growing (acquiring) a “Know-how” knowledge base. The suggested system can be built using any of the following techniques: rule-based techniques, inductive techniques, symbol manipulation techniques, case-based techniques, and/or qualitative techniques (such as model-based, temporal, reasoning, and neural networks). It will have an inference engine that will reason and search (compose) solutions. It will also have a learning module (knowledge acquisition) that will allow the system to learn and improve its performance through exposure to learning opportunities (contexts and decisions). It will also have an explanation module that would allow answers for reasoning questions such as: Why, What if, What is, How, and Why not. The “Know-how” knowledge base is a sort of a decision support system for those who may not have the technical or strategic experience necessary to chart an effective roadmap to uncover the valuable predictive insights hidden within their existing data. The “know-how” knowledge base will provide: · How and where to get started with a specific data set · Causes of failure to straight forward application of a specific tool, and how pitfalls can be avoided · Relevant Case studies that reveal the rewards of proper algorithm selection, proper design and careful implementation when dealing with a specific data set · Why establishing an internal predictive modeling practice is within one’s reach – will also establish a roadmap for a specific data set · Tips, tricks and techniques for a specific data set preparation, method selection, validation methods, and gluing of appropriate data mining, statistical, and visualization methods (making use of the stacking approach) · Interactive guru session with explanations · Resources (meta-knowledge) and direction on how to move forward with confidence
|
|
|
The PSU Hub will offer a data mining certificate similar to the following international certificates: -University of California San Diego- Data mining certificate -University of Connecticut- Data Mining online Master Degree and Graduate Certificate -Stanford University- Data Mining and Applications graduate certificate -SAS and Oklahoma State University Data Mining certificate (This is a jopint Certificate with SAS) -University of Louisville graduate certificate in data Mining -New jersey’s Science & Technology University certificate in data Mining.
PSU is to seek offering certification in Data Mining with the collaboration of either SAS Saudi Arabia or IBM Saudi Arabia. SAS Saudi Arabia is currently offering certificates in Statistics with both King Fahd University for Petroleum and Mineral and King Saud University. PSU can start the first “Data Mining” certificate with SAS. IBM Saudi currently provides two Data Mining solutions: “Cognos” and “DB2”. PSU can offer joint Data Mining certificate with IBM utilizing these two packages. The requirements for any of these certifications can be: § 12 credit hours § 1 year to complete (on average) § Graduate Certificate may qualify for Financial Assistance What are the required courses? · Data Management System Design · Data Mining and Management o Select two(2) from: · JAVA Programming · Advanced Database Systems · Information Retrieval · Knowledge Based Systems Courses can be offered both in class and on-line. Also see “Learning Outcomes” of the Certificate in the appendix. |
|
|
Completing the Certificate is equivalent to getting a PSU Graduate Diploma. The Diploma qualifies students to enter into PSU Master program in Information Systems or Computer Science. The PSU Master degree can be built incrementally starting with both the “Netversity” Certificate Diploma holders and the SAS-PSU or IBM-PSU Data Mining Diploma holders.
PSU can also establish a link with similar programs such as the Oklahoma State University – SAS program and arrange for PSU graduates to continue their post graduate degrees in OSU (courses at PSU and research theses at OSU) |
|
|
Applied Research
|
|
The following are some Application areas that the Hub will focus upon (Suitable for Saudi Arabia):
-Finance: Credit Card Analysis
-Insurance: Claims, Fraud Analysis
-Telecommunication: Call Record Analysis
-Transport: Logistics management
-Consumer Goods: Promotion Analysis
-Data Service Providers: Value Added Data
-Utilities: Power Usage Analysis
-Medicine: Effectiveness of treatments & relationship between diseases
The following diagram shows the percentages of “data Mining” sectors in each area worldwide.
Here are some examples of “Data Mining” Applications (questions) in the above sectors:
z Performing basket analysis
y Which items customers tend to purchase together. This knowledge can improve stocking, store layout strategies, and promotions.
z Sales forecasting
y Examining time-based patterns helps retailers make stocking decisions. If a customer purchases an item today, when are they likely to purchase a complementary item?
z Database marketing
y Retailers can develop profiles of customers with certain behaviors, for example, those who purchase designer labels clothing or those who attend sales. This information can be used to focus cost–effective promotions. Also can recongise sales patterns amamong outlets, so identify trends and shifts in customers taste
z Merchandise planning and allocation
y When retailers add new stores, they can improve merchandise planning and allocation by examining patterns in stores with similar demographic characteristics. Retailers can also use data mining to determine the ideal layout for a specific store.
z Card marketing
y By identifying customer segments, card issuers and acquirers can improve profitability with more effective acquisition and retention programs, targeted product development, and customized pricing.
z Cardholder pricing and profitability
y Card issuers can take advantage of data mining technology to price their products so as to maximize profit and minimize loss of customers. Includes risk-based pricing.
z Fraud detection
y Fraud is enormously costly. By analyzing past transactions that were later determined to be fraudulent, banks can identify patterns. They can isolate the factors that lead to fraud, waste and abuse. They can also target auditing and investigative efforts more effectively. By modeling each credit card holder’s requested transactions against the customer’s past spending history, fraud transactions can be identified. Also can idefify health insurance fraud by mining insurance claims including laboratory tests and identify un-needed expensive tests. Also can check information on new card applications against data from Credit Bureaus. To stop new accounts. Also from an online stream of events identify fraudulent events.
y
z Predictive life-cycle management
y DM helps banks predict each customer’s lifetime value and to service each segment appropriately (for example, offering special deals and discounts).
z Call detail record analysis
y Telecommunication companies accumulate detailed call records. By identifying customer segments with similar use patterns, the companies can develop attractive pricing and feature promotions.
z Customer loyalty
y Some customers repeatedly switch providers, or “churn”, to take advantage of attractive incentives by competing companies. The companies can use DM to identify the characteristics of customers who are likely to remain loyal once they switch, thus enabling the companies to target their spending on customers who will produce the most profit.
z Customer segmentation
y All industries can take advantage of DM to discover discrete segments in their customer bases by considering additional variables beyond traditional analysis. This can be used for recruiting and attracting customers; identify profitable customers.; and build profiles of customers likely to use which services.
z Manufacturing
y Through choice boards, manufacturers are beginning to customize products for customers; therefore they must be able to predict which features should be bundled to meet customer demand. Quality Control: - building predictive models for the effects of production parameters on produced items’ performance.
z Warranties
y Manufacturers need to predict the number of customers who will submit warranty claims and the average cost of those claims. Also Parts failure prediction.
z Frequent flier incentives
y Airlines can identify groups of customers that can be given incentives to fly more.
z Given a database of 100,000 names, which persons are the least likely to default on their credit cards?
z Which types of transactions are likely to be fraudulent given the demographics and transactional history of a particular customer?
z If I raise the price of my product by Rs. 2, what is the effect on my ROI?
z If I offer only 2,500 airline miles as an incentive to purchase rather than 5,000, how many lost responses will result?
z If I emphasize ease-of-use of the product as opposed to its technical capabilities, what will be the net effect on my revenues?
z Which of my customers are likely to be the most loyal?
z `Forecasting what may happen in the future
z Classifying people or things into groups by recognizing patterns
z Clustering people or things into groups based on their attributes
z Associating what events are likely to occur together
Sequencing what events are likely to lead to later events. E.g. Credit/Risk Scoring and Intrusion Detection.
1. Health Care: What percentage of people in the test group have high blood pressure with these characteristics: 66-year-old male regular smoker that has low to moderate salt consumption?
2. Do the risk levels change for a male with the same characteristics who quit smoking? What are the percentages?
3. If you are a 2% milk drinker, how many factors are still interesting?
4. Knowing that salt consumption and smoking habits are interesting factors, which one has a stronger correlation to blood pressure levels?
Grow an automatic tree. Look to see if gender is an interesting factor for 55-year-old regular smoker who does not each cheese?
|
Funding of Projects |
PSU Hub will sponsor research funds and grants for outstanding research projects, which can create value to the Kingdom of Saudi Arabia and society. These research grants will be awarded for high-level and promising Data Mining research projects by individuals or groups from academia and/or industry actively involved in the development and research. These projects should be based on either a universally known technology or a new technology developed by the applicant and should be aimed at achieving viable systems, algorithms, or processes beneficial to the nation. The grant will be provided for a period of 3 or 6 Months depending on the scope of the project.
Faculty members and Research staff of all faculties of PSU as well as all universities of the Kingdom of Saudi Arabia can apply for this research grant, however the scope of their proposed project should be in any aspect of Data Mining. The principal investigator should hold doctorate degree and he/she should have a solid background in the proposed research with well-reputed international and national journal and conference publications. The projects which will be jointly pursued with industrial government, and commercial organizations will be given higher priority. All submitted proposals will be peer-reviewed/evaluated by a panel of experts and will be approved on their originality, novelty, relevance, significance, and quality, etc.
• Solving a national problem
• Developing a specific algorithm, process or patent
• Investigating data mining in the scope of the
center’s research areas (provided in the application form)
• Short-period time frame
|
Research Type |
Total |
1st Deposit |
2nd Deposit |
At Publication |
No. of Papers |
|
Small (3 Months) |
30000 |
10000 |
10000 |
10000 |
1 |
|
Medium (6 Months) |
60000 |
20000 |
20000 |
20000 |
2 |
|
English & Arabic Data Mining Digital Libraries |
One of the main objectives of the Hub is to strive to increase the quantity and quality of Arabic articles in the area of “Data Mining” on the Web. All published activities from the Hub will be translated and reviewed by its author(s) to be available also in an Arabic Digital Library. A systematic plan to translate many “data mining” articles and storing them in a searchable Arabic Digital Library will be developed. Parallel to this an English Data Mining digital library will be developed. Both libraries will have traditional search engine beside more elaborated text mining capabilities.
Text and Multi-media mining tools will be used to explore the digital libraries contents and expose related and correlated paragraphs and sections. Text mining is used to find interesting regularities in large textual digital libraries. Where interesting means: non-trivial, hidden, previously unknown and potentially useful. Text mining tools handle digital libraries text at the word level, sentence level, document level, document-collection level, linked-document collection level, and at the application level. Most of the text mining methods reply on the fact that there is usually high redundant data in the documents. Most of the tools make use of: document summarization techniques, single document graph visualization algorithms, segmentation algorithms, features selection algorithms, similarity algorithms, clustering, and information extraction techniques. They also make use of several visualization techniques such as: WebSOM, ThemeScape, Graph-Based visualization techniques, and Tiling-based visualization techniques.
Statistical tools for text mining include: Yale/Rapid Miner word vector mining, UIMA by IBM, GATE, Aero Text suite, Attensity, Endeca Technologies, Inxight, and Language Ware.
Similar to what we provide for “Data Mining” we also provide the same vertical stacking of text Mining, statistical, and visualization algorithms since performing text mining to both the English and the Arabic digital libraries will provide an interesting context for researchers in “Text mining” and “Arabization” to investigate how to improve the Arabic text mining algorithms and use a cross reference to the English one. A very interesting research direction can be developed there. For example, the same mining questions can be posed to both the English and the Arabic digital libraries and the results can be compared. In cases of differences, learning opportunities will be developed and algorithms’ modifications and enhancements are to be investigated. The two libraries will provide several ways and means for verification, validation, cross checking. This will be sort of an experimental testbed or sandbox for testing and experimentation.
|
Free Webinars |
The PSU Hub will schedule Web broadcasts of its meetings and conferences. Other Web webcasts will be announced and followed such as:
· SAS Webcasts on Analytics, Predictive Modeling and Data Mining.
·
Predictive
Analytics Applied - Immediate access, on-demand
These are self-paced online courses that cover predictive analytics
applications, core technology, and management. Detailed case studies and
software demos are included.
|
Training |
In addition to the Cerificate and Diploma in Data Mining, custom-made courses could be arranged by the Hub to be delivered to specific targeted audience. It is important to know that Data Mining is not:
z Brute-force crunching of bulk data
z “Blind” application of algorithms
z Going to find relationships where none exist
z Presenting data in different ways
z A database intensive task
z A difficult to understand technology requiring an advanced degree in computer science
Experience gained from Training will be documented in “Know-How” knowledge base, and will be added experience to the target knowledge base.
|
Consulting |
The Hub will provide several consulting services such as Developing strategic plans for data mining, reviewing and evaluating the feasibility of applying data mining techniques to an enterprise, providing trained human resources, developing enterprise procedures and systems for data mining and knowledge discovery.
Experience gained from consulting will be documented in “Know-How” knowledge base, and will be added experience to the target knowledge base.
|
Solutions |
The Hub can offer special presentations of its current “Know-how” knowledge base. The benefit of these presentation is that they will provide alternative available solutions and when and how they will be applied; How and where to get started; Why failure to implement is so common, and why pitfalls are so avoidable; Case studies that reveal the rewards of proper design and implementation; Why establishing an internal predictive modeling practice is within one’s reach; Tips, tricks and techniques for data preparation and method selection; Live participant polls and an interactive guru session with the experts; Resources and direction on how to move forward with confidence
Experience gained from developed solutions will be documented in “Know-How” knowledge base, and will be added experience to the target knowledge base.
|
Experience |
Accumulated experience of the Hub personal will be documented in “Know-How” knowledge base, and will be added experience to the target knowledge base.
|
Business Analytics |
Experience gained from business cases will be documented in “Know-How” knowledge base, and will be added experience to the target knowledge base.
|
Business Intelligence |
Experience gained from consulting and running business intelligence projects will be documented in “Know-How” knowledge base, and will be added experience to the target knowledge base.
|
Meetings |
Experience gained from mettings will be documented in “Know-How” knowledge base, and will be added experience to the target knowledge base.
|
Conferences |
|
Data Repositories |
Examples of existing Data repositories:
· KDD Cup center, with all data, tasks, and results.
· UCI KDD Database Repository for large datasets used in machine learning and knowledge discovery research.
· UCI Machine Learning Repository.
· AWS (Amazon Web Services) Public Data Sets, provides a centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications.
· Bioassay data, described in Virtual screening of bioassay data, by Amanda Schierz, J. of Cheminformatics, with 21 Bioassay datasets (Active / Inactive compounds) available for download.
· Data.gov.uk, publicly available data from UK.
· DataFerrett, a data mining tool that accesses and manipulates TheDataWeb, a collection of many on-line US Goverment datasets.
· Delve, Data for Evaluating Learning in Valid Experiments
· Enron Email Dataset, data from about 150 users, mostly senior management of Enron.
· FEDSTATS, a comprehensive source of US statistics and more
· FIMI repository for frequent itemset mining, implementations and datasets.
· Financial Data Finder at OSU, a large catalog of financial data sets
· GEO (GEO Gene Expression Omnibus), a gene expression/molecular abundance repository supporting MIAME compliant data submissions, and a curated, online resource for gene expression data browsing, query and retrieval.
· Grain Market Research, financial data including stocks, futures, etc.
· ICWSM-2009 dataset contains 44 million blog posts made between August 1st and October 1st, 2008.
· Infobiotics PSP (protein structure prediction) datasets, adjustable real-world family of benchmarks for testing the scalability of classification/regression methods.
· Investor Links, includes financial data
· Microsoft's TerraServer, aerial photographs and satellite images you can view and purchase.
· MIT Cancer Genomics gene expression datasets and publications, from MIT Whitehead Center for Genome Research.
· NASDAQ Data Store, provides access to market data.
· National Government Statistical Web Sites, data, reports, statistical yearbooks, press releases, and more from about 70 web sites, including countries from Africa, Europe, Asia, and Latin America.
· National Space Science Data Center (NSSDC), NASA data sets from planetary exploration, space and solar physics, life sciences, astrophysics, and more.
· PubGene(TM) Gene Database and Tools, genomic-related publications database
· SMD: Stanford Microarray Database, stores raw and normalized data from microarray experiments.
· SourceForge.net Research Data, includes historic and status statistics on approximately 100,000 projects and over 1 million registered users' activities at the project management web site.
· STATOO Datasets part 1 and STATOO Datasets part 2
· UCR Time Series Classification/Clustering page, offering datasets, papers, links, and code.
· United States Census Bureau.
|
Forums |
Experience gained from forums’ discussions will be documented in “Know-How” knowledge base, and will be added experience to the target knowledge base.
|
FAQs |
|
CFP |
Links to most updated “Call for Papers” in the field.
|
International Conference |
|
Partners & Collaborations |
Current possibilities:
OSU-SAS
SAS
IBM
|
Students Activities |
Students can work as:
-Research assistances
-help in organizing Free Webinars
-help in Training
-help in Business Analytics
-help in Business Intelligence
-help in organizing Meetings
-help in organizing Conferences
-help in maintaining Data Repositories
-help in moderating Forums
-help in seeking Partners & Collaborations
-help in maintaining the Arabic Digital Library
-help in moderating Membership of Social Networks
-help in maintaining the Membership of the Hub
-help in establishing Links to other Universities offering degrees in “Data Mining”
-help in providing and broadcasting Hub News
|
Member of Social Networks |
In order to have more exposure, the Hub will become a member of several current social networks such as:
F
ollow Us on...
Main Menu
|
Become a Member of the Hub |
Membership Goals:
· Building database of professionals in “Data Mining”
· Encouraging information exchange among members
· Encouraging researchers to publish in “Data Mining” field.
· Supporting Arabic content in Data Mining.
· Providing knowledge in Data Mining.
· Establishing a relationship between professionals and stakeholders.
Membership Benefits:
· Having the latest news and events supported by the Hub
· The possibility of participating in research and consultations projects.
· The possibility of getting special rate in training which carried out by the Hub
· The possibility of funding member’s research.
· The possibility of funding member’s publications.
· Receiving news and awareness information.
· Receiving update information on Data Mining conferences.
· The possibility of accessing to the Hub’s Arabic Digital Library.
· The possibility of having discounts by Hub sponsors
· Receiving messages related to events.
Membership Conditions:
· Providing true and accurate information.
· Participating in publishing articles and research on the Hub’s website.
· Supporting other Hub members.
· Present the Hub works to professionals; and working hard to attract research opportunities to the Hub.
|
Links to Data Mining Research Groups |
|
Links to Other Universities Offering “Data Mining” Degrees |
|
Hub News |
FeedBurner makes it easy to receive content updates in My Yahoo!, Newsgator, Bloglines, and other news readers. It works with web-based news readers such as:
|
Start Up Projects at PSU Data Mining Hub |
--You can download a PPT that describes the projects below
Attached are two Start up projects for the PSU Data mining Hub. They are:
1-Medical Data mining & Knowledge Discovery- “Constructing Rule-Based Knowledge Bases Using Ant Colony Optimization”:
Medical service in the Kingdom has gone through several leaps of improvement in the recent few years. Currently medium to large hospitals have established several kinds of modern information systems to keep track of their operations. Computerized information systems are in place to keep records of patients, operations, materials, tests, procedures, medications, facilities, etc. With the heavy traffic that goes through such hospitals, they are currently overwhelmed with such amounts of data. They certainly need guidance and results to make effective use of such data-rich records, yet information-poor. There is so much one can reveal from the amount of accumulated data that hospitals acquire over the years. In this project we propose a Self-organizing Ant Colony Optimization (ACO) technique that is inspired by the behavior of the ants as social insect that work together to accomplish a common goal using wisdom of the crowd. ACO is one of the algorithms that put swarm intelligence into action. Swarm intelligence, which is based on the idea of collective behavior, has occupied ACO in various fields and problem solving domains. Data mining is one of the domains where ACO has been applied successfully and provided scalable solutions. In this project, we describe a knowledge discovery classification technique based on ACO. AntMiner, is a rule induction algorithm that occupies collective intelligence to construct classification rules. Experimental results are shown as the AntMiner+ is implemented with different variations inspired from discrete optimization, fuzzy rule induction, self-organizing map (SOM), dimensionality reduction, parallel simultaneous rule learning and tested on different datasets. Moreover, further combinations of these variations that produced enhancement are also proposed and tested.
--- Ahmed-Kamal file (This file I will keep updating each week ISA)
2-Satellite Data Mining:
This is an Image Mining research proposal.
This project proposes a novel approach for monitoring “hot points” on Saudi Arabia ground. Hot points, are the locations on ground such as boards with neighbours, places which have the most frequent visits from spy orbiting satellites, oil fields and religious places. These points should be tightly secured for Saudi national security. This work will provide results of data mining satellite images of these points over any specified period of time to be used by decision makers in defence and oil exploration.
Currently there are three Saudi’s satellites (Saudisat `A, 1B, and 1C) also known by Saudi Osxar 41, 42, and 50. They are owned by King Abdel Aziz for Science and Technology. We need to see how to use them wisely in such project. We first need to know what they are currently used for and the kind of research done by them. There are many web info about them. We need to establish some sort of linkage with researchers at this institute.
---Faisal-Ali file (This file I will keep updating each week ISA)
3- Development of Arabic Text Mining Software Tools:
---Omar-file (This file I will keep updating each week ISA)
Software tools for English Text Mining:
http://www.kdnuggets.com/software/text.html
Objective:
Is to adapt and modify selected English Text Mining tools (from the above web site) in order to produce their equivalent Arabic versions. The cross validation method requires very accurate English/Arabic translator that will provide input data to the Algorithm/program conversion.
English/Arabic Translators:
They vary in their accuracy. Some sites:
http://translation.babylon.com/english/to-arabic/
Arabic Natural Language Processing:
Arabic Text Mining open source
Methodology:
The second objective is to strive to improve the quantity and quality of Arabic contents in the area of “Data and Text Mining” on the Web. All published material from the Hub’s activities will be translated and reviewed by its author(s) to be available in an Arabic Digital Library. A systematic plan to translate many “data mining” articles and storing them in a searchable Arabic Digital Library will be developed. Text and Multi-media mining tools will be used to explore this Arabic digital library contents and expose related and correlated paragraphs and sections for the purpose of developing new Arabic Text mining algorithms and enhance exiting ones. This brings the other area of focus of the Hub which is the unstructured Text mining.
As for the Unstructured Text mining: Parallel to the Arabic digital library there will be also an English Data Mining digital library (having the same contents) that will be developed. Both libraries will have traditional search engine beside more elaborated classification and categorization capabilities. Further to this, Text and Multi-media mining tools will be used to explore the two digital libraries contents and expose related and correlated paragraphs and sections. Text mining is used to find interesting regularities in large textual digital libraries. Where interesting means: non-trivial, hidden, previously unknown and potentially useful. Both Arabic and English Text mining tools handle digital libraries text at the word level, sentence level, document level, document-collection level, linked-document collection level, and at the application level. Most of the text mining methods reply on the fact that there is usually high redundant data in the documents. Most of the tools make use of: document summarization techniques, single document graph visualization algorithms, segmentation algorithms, features selection algorithms, similarity algorithms, clustering, and information extraction techniques.
They also make use of several visualization techniques such as: WebSOM, ThemeScape, Graph-Based visualization techniques, and Tiling-based visualization techniques.
Statistical tools for text mining include: Yale/Rapid Miner word vector mining, UIMA by IBM, GATE, Aero Text suite, Attensity, Endeca Technologies, Inxight, and Language Ware.
Similar to what we provide for “Data Mining” we also propose the same vertical stacking of text Mining, statistical, and visualization algorithms for performing text mining to both the English and the Arabic data mining digital libraries. This will provide an interesting context for researchers in “Text mining” and “Arabization” fields to investigate how to improve the Arabic text mining algorithms and use a cross reference to the English ones. A very interesting research direction can be developed there. For example, the same mining questions can be posed to both the English and the Arabic digital libraries and the results can be compared. In cases of differences, learning opportunities will be developed and algorithms’ modifications and enhancements are to be investigated. The two libraries will provide several ways and means for verification, validation, and cross checking
Arabicl-Template-PSU Research Proposal.docx
4- Know-how Knowledge-based System:
Extra Appendix:
|
Learning Outcomes: Certificate or Diploma in Data Mining
Course Notes:
below
Assignments,
Mid-term Quiz, and Final Exam
Datasets
Assign-1-solution
Assign-2-solution
Assign-3-solution
Assign-4-solution
Assign-5-solution
Grade
Distribution:
Assignments
15%
Term
Project 30%
Midterm
15%
Final
40%
. |