CS548 Knowledge Discovery and Data Mining 
Showcase Presentation Schedule - Spring 2015

PROF. CAROLINA RUIZ 

WARNING: Changes to this schedule may be made during the course of the semester. 

Instructions:

1. Sign up for a showcase topic of your interest. Check space availability for each topic in the schedule below.

2. Work together with the group of students assigned to the same topic to identify a real-world application of the data mining topic you are assigned to.

3. Discuss your chosen data mining application with the professor at least 2 weeks in advance to the presentation. You need to get the professor's approval of your selected application before you start preparing your presentation.

4. The team should investigate the application in depth and prepare and deliver a 15 minute in-class presentation describing this application in as much detail as possible, focusing on its data mining aspects.

5. Your presentation should contain the following sections:
   • A cover page with the following title and subtitle, replacing the parts in red with the information for your particular showcase:

     CS548 Spring 2015 <Data Mining Technique> Showcase by < students' names >
     Showcasing work by < application authors or company > on <"Title or name of the application you are showcasing" >

   • A list of references and resources that you used for your presentation. This should be included right after the cover page. If you used articles and research papers, include the full reference not just a link to the articles. For this, follow the IEEE formatting rules available at IEEE citation style. Follow this format style to reference books, journal articles, conference articles, online references, and other published or unpublished work. The richer your set of references, the better.
   • A detailed description of the application.

6. Email the following materials to the cs548-staff email address at least 48 hours in advance to your class presentation.
   • Your presentation slides. Please name your representation slides as follows:

     CS548S15_Showcase_<Data Mining Techique>.<file extension>

     If at all possible, please send us the slides in an editable format (e.g., pptx) so that we can make small edits if needed.
   • A short description of your application (3-4 sentences) to be included in this webpage under "Short Description" in your showcase entry below.

7. Rehearse your oral presentation to make sure it is polished, transitions between speakers work well, and the full presentation stays within the time allowed (15 minutes).

Feb. 17

• Data Mining Technique / Area: Decision Trees
• Students: Cody Olivier, Lily Amadeo, Suman Lama, Bir Kafle.
• Application Topic: Real-Time Human Pose Recognition Using Decision Trees
• Short description:
  The Microsoft Kinect is a TV-mounted camera which tracks bodies in real time for use with XBox 360 and XBox One games and applications. It uses decision forests to calculate particular body parts from what it sees ( what's a head, torso, arm, etc. ). Not only is this a break through in real-time human recognition, but the Kinect can also classify body parts from images it takes quickly enough to be used in gaming and other real-time applications.
• Slides: CS548S15_Showcase_Decision_Trees

Feb. 24

• Data Mining Technique / Area: Model and Regression Trees
• Students: Viseth Sean, Azharuddin Priyotomo, Yang Liu.
• Application Topic:
• Short description:
  This paper uses model trees "... for monitoring and modeling the natural changing and complex characteristics of urban dynamics where most cultural, social and economic activities take place."
• Slides: CS548S15_Showcase_Model_Trees

Mar. 3

• Data Mining Technique / Area: Association Rules I
• Students: Marcus Moyses, Cory Hayward.
• Application Topic: Finding Ideal Menu Items Assortments
• Short description:
  A Japanese-style restaurant in Taiwan uses a data mining technique known as association rules to recommend ideal combinations of entrées and appetizers to customers.
• Slides: CS548S15_Showcase_Association_Rules

Mar. 24

• Data Mining Technique / Area: Clustering I
• Students: Guojun Wu, Xinyu Dai, Jiacheng Wang.
• Application Topic: Optimizing User Exploration Experience in Emerging E-Commerce Products
• Short description:
  Categories used in app stores are usually poorly organized and not intuitive. The paper in this showcase develops a hierarchical clustering model to generate categories automatically from users' search logs. This approach not only provides a well-defined structure, which can help save tedious human effort in processing data, but the clustering can also help users reach products of their interest efficiently resulting in a better user experience.
• Slides: CS548S15_Showcase_Clustering_I

Mar. 31

• Data Mining Technique / Area: Clustering II
• Students: Michael Barry, Cheng Deng, Junwei Guan, Xing Liu, Robert Van Reenen.
• Application Topic: Inferring Road Networks from GPS Traces
• Short description:
  Mapmaking has typically been a very manual, labor intensive process. However, over the past few year there has been an explosion in the number of devices that produce GPS traces for cars, buses, people, and other moving objects. The papers investigated in this showcase survey the current state-of-the-art techniques for automatically inferring road networks from sparse and noisy GPS data using both density and centroid based clustering algorithms.
• Slides: CS548S15_Showcase_Clustering_II

Apr. 7

• Data Mining Technique / Area: Anomaly Detection
• Students: Nitish Bahadur, Caitlin Kuhlman, Gulsher Kooner.
• Application Topic: Anomaly-based etwork Intrusion Detection (A-NIDS)
• Short description:
  In the wake of many high profile data breaches, it is clear that cyber security is an important issue. Network Intrusion Detection Systems (NIDS) are one important tool to combat threats against a network, and the use of anomaly detection to identify previously unseen attacks has been extensively researched. We will present a classic anomaly based NIDS from the University of Minnesota, and also a more advanced platform for cyber defense Palintir Gotham.
• Slides: CS548S15_Showcase_Anomaly_Detection

Apr. 14

• Data Mining Technique / Area: Text Mining
• Students: Joseph True, Suwodi Dutta Bordoloi, Chitra Pichaimuttu Kanickaraj. No more students
• Application Topic: IBM.s Watson and Jeopardy!
• Short description:
  This presentation provides an overview about IBM Watson - the open-domain question answering (QA) computer system that competed and won against human contestants on the TV quiz game show Jeopardy! in 2011. The presentation describes the overall system and related technologies. We also take a closer look at some of the Text Mining approaches and techniques used by the system to analyze questions and then search, find, and rank answers. The supporting information for the presentation was based on a collection of research papers published in the IBM Journal of Research and Development.
• Slides: CS548S15_Showcase_Text_Mining

Apr. 21

• Data Mining Technique / Area: Sequence Mining
• Students: Pankaj Didwania, Sarah Schultz, Mingchen Xie.
• Application Topic: Temporal Event Sequence Mining for Glioblastoma Survival Prediction
• Short description:
  This presentation showcases a paper that uses sequence mining and other data discovery techniques to predict whether patients with glioblastoma, an aggressive form of brain cancer, will survive for at least one year. The authors are able to discover certain traits of patients and patterns of treatment that are indicative of survival longer than the median rate of 12 months.
• Slides: CS548S15_Showcase_Sequence_Mining

Apr. 28

• Data Mining Technique / Area: Web Mining
• Students: Salah Ahmed, Hai Liu, Shaocheng Wang, Sijing Yang.
• Application Topic: Amazon.com's Recommender System
• Short description:
  Amazon's Recommendation System Amazon uses Item by Item Collaborative Filtering Algorithm to recommend products to its online customers. Amazon's recommendation system is proved to be one of the most successful recommendation system in the world.
• Slides: CS548S15_Showcase_Web_Mining