Download Mining Regional Knowledge in Spatial Dataset

What Else is Important in AI we Did not Cover?
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Ontologies and the Semantic Web
Logical Reasoning and Theorem Proving
Distributed Artificial Intelligence
Multi-Agent Systems
Robotics
Philosophical Foundation of AI
Natural Language Understanding
Knowledge in Learning
Department of Computer Science
UH-DAIS
Data Analysis and Intelligent Systems Lab
Its research is focusing on:
1. Spatial Data Mining
2. Clustering and Anomaly Detection
3. Classification and Prediction
4. GIS
Current Projects
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Clustering Algorithms with Plug-in Fitness Functions and Other NonTraditional Clustering Approaches
Analyzing and Doing Useful Things with Bio-aerosol Data
Interestingness Scoping Algorithms for the Analysis of Spatial and
Spatio-temporal Datasets
Using Mixture Models for Anomaly Detection and Change Analysis
Taxonomy Generation—Learning Class Hierarchies from Training Data
Educational Data Mining (lead by Nouhad Rizk)
Department of Computer Science
UH-DAIS
Looking for 1-2 Students for Master Thesis
Students should begin working on their thesis Jan. 16 or May 31, 2017:
Areas of Interest include:
1. Spatio-Temporal Clustering, Interestingness Hotspot Discovery,
and Change Analysis in Spatial Datasets
2. Design and Implementation of a Water Level Prediction and
Flood Warning System for Harris County
3. Disaster Computing—Using AI Planning for Absorbing and
Recovering from Critical Component Failures, starting May 31,
2016.
4. Educational Data Mining: Early Warning System for Failing
Students/Student Self-Assessment System
5. Event Detection in Spatio-temporal Datasets already gone!
If you are interested, send me an e-mail by December 31, 2016,
and I will be selecting students by January 15, 2017. Send me an email, even if you want to start June 1, 2017!
Department of Computer Science
UH-DAIS
Interestingness Hotspot Discovery
Framework
Identify
hotspot
seeds
Air pollution dataset
low-variation hotspots
Grow hotspot
seeds by adding
neighboring
objects
Earthquake dataset
correlation hotspots
Remove redundant
hotspots using a
graph-based
approach
Find
scope of
hotspot
s
Objectives
Objectives
Find interesting contiguous regions in spatial
data sets based on the domain expert’s notion
of interestingness which is captured in an
interestingness
functionfunctions
Allow plugin
interestingness
to be used
with point based, polygonal or
gridded
datasets
Develop
algorithms
to create
Gridded
dataset
neighborhood graph
for point based datasets.
Remove redundant overlapping
hotspots
and find the scope of each hotspot.
Point-based
and Polygonal
datasets
By Fatih Akdag and Christoph F. Eick
Spatio-Temporal Clustering
Remark: Future Research will also investigate Spatio-Temporal Event Detection
Analyzing NYC Cab Pickup Data
People: Yongli Zhang and Karima Elgarroussi
Department of Computer Science
UH-DAIS
1d: Spatio-temporal Event Detection
Example: Event Detection System Architecture
Department of Computer Science
Educational Data Mining (EDM)
People: Nouhad Rizk, Karthik Bibireddy, Rohith Jidagam and Alex Lam
Department of Computer Science
UH-DMML