Data Mining Research

Recently, data mining has attracted much attention in the database community. Data mining refers to the process of extracting hidden information from databases. It can be thought of as a step in the knowledge discovery process. There are many data mining techniques. The SMUDB group's current research into Data Mining centers around modeling of stream data, rare event detection, and visualization.

CURRENT RESEARCH:

• Modeling Stream Data
• Extensible Markov Model (EMM)
• Concept Drift
• Rare Event Detection
• Intrusion Detection
• Visualization
• Temporal Chaos Game Representation (TCGR)

PREVIOUS RESEARCH:

• Clustering
• Online Clustering
• Dynamic Linear Condensation Technique
• How to Handle Outliers
• Association Rules
• Online and Adaptive Association Rule Mining
• Combine Clustering & Association Rules
• Online and Adaptive
• Geospatial Research
• NSF Grant No. 9820841
• GOALI (Grant Opportunity for Academic Liaison with industry)
• SIVAM (System for the Vigilance of the Amazon)
• Raytheon Systems Company, Garland Division
• Prediction Algorithms to predict environmental catastrophies
• Spatial Temporal Association Rules
• Flood Prediction
• Spatially and Temporally Distributed Data
• Hidden Markov Model (HMM)