A5071546444- Data Mining Algorithms and Applications
- Rough Sets and Fuzzy Logic
- Data Management and Algorithms
- Anomaly Detection Techniques and Applications
- Complex Network Analysis Techniques
- Network Security and Intrusion Detection
- Climate variability and models
- Advanced Graph Neural Networks
- Time Series Analysis and Forecasting
- Spam and Phishing Detection
- Advanced Clustering Algorithms Research
- Text and Document Classification Technologies
- Machine Learning and Data Classification
- Energy Load and Power Forecasting
- Web Data Mining and Analysis
- Imbalanced Data Classification Techniques
- Atmospheric and Environmental Gas Dynamics
- Meteorological Phenomena and Simulations
- Hydrological Forecasting Using AI
- Internet Traffic Analysis and Secure E-voting
- Remote Sensing in Agriculture
- Bioinformatics and Genomic Networks
- Hydrology and Watershed Management Studies
- Misinformation and Its Impacts
- Domain Adaptation and Few-Shot Learning
Michigan State University
2016-2025
Renmin University of China
2025
Michigan United
2018
Samsung (United States)
2015
Novo Nordisk (Denmark)
2010
Institute of Electrical and Electronics Engineers
2006
University of Minnesota
1996-2005
University of Minnesota System
2003-2005
Twin Cities Orthopedics
2003
Web usage mining is the application of data techniques to discover patterns from data, in order understand and better serve needs Web-based applications. consists three phases, namely preprocessing, pattern discovery , analysis . This paper describes each these phases detail. Given its potential, has seen a rapid increase interest, both research practice communities. provides detailed taxonomy work this area, including efforts as well commercial offerings. An up-to-date survey existing also...
Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in data set. For example, metrics such as support, confidence, lift, correlation, collective strength are often used determine interestingness of patterns. However, many measures provide conflicting information about pattern, best use given application domain is rarely known. In this paper, we present an overview various proposed statistics, machine learning...
Abstract Understanding the factors that affect water quality and ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how will respond to changes not only requires data, but also information about context of individual bodies across broad spatial extents. Because lake usually sampled in limited geographic regions, often for time periods, assessing controls compilation many data sets regions into integrated database. LAGOS-NE accomplishes...
A new Unmanned Aerial Vehicle (UAV) Synthetic Aperture Radar (SAR) has been developed at Multimedia University, in collaboration with Agency of Remote Sensing Malaysia.The SAR operates C-band, single V -polarization, 5 m × spatial resolution.Its unique features include compact size, light weight, low power and capable performing real-time imaging.A series field measurements flight tests conducted good quality images have obtained.The system will be used for monitoring management earth...
Climate change is predicted to intensify lake algal blooms globally and result in regime shifts. However, observed increases biomass do not consistently correlate with air temperature or precipitation, evidence lacking for a causal effect of climate the nonlinear dynamics needed demonstrate We modeled effects on annual chlorophyll (a measure biomass) over 34 y 24,452 lakes across broad ecoclimatic zones United States evaluated potential found that was causally related 34% lakes. In these...
Existing association-rule mining algorithms often rely on the support-based pruning strategy to prune its combinatorial search space. This is not quite effective for data sets with skewed support distributions because they tend generate many spurious patterns involving items from different levels or miss potentially interesting low-support patterns. To overcome these problems, we propose concept of hyperclique pattern, which uses an objective measure called h-confidence identify strong...
To analyze the effect of oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize behavior selected regions Earth's atmosphere. In past, scientists used observation and, more recently, eigenvalue analysis techniques, such as principal components (PCA) singular value decomposition (SVD), to discover indices. However, techniques only useful for finding a few strongest signals. Furthermore, they impose condition all discovered...
Current outlier detection schemes typically output a numeric score representing the degree to which given observation is an outlier. We argue that converting scores into well-calibrated probability estimates more favorable for several reasons. First, allow us select appropriate threshold declaring outliers using Bayesian risk model. Second, obtained from individual models can be aggregated build ensemble framework. In this paper, we present two methods transforming probabilities. The first...
Abstract Ecosystem scientists have yet to develop a proven methodology monitor and understand major disturbance events their historical regimes at global scale. This study was conducted evaluate patterns in an 18‐year record of satellite observations vegetation phenology from the Advanced Very High Resolution Radiometer (AVHRR) as means characterize ecosystem regimes. The fraction absorbed photosynthetically active radiation (FPAR) by canopies worldwide has been computed monthly time...
Previous chapter Next Full AccessProceedings Proceedings of the 2009 SIAM International Conference on Data Mining (SDM)Detection and Characterization Anomalies in Multivariate Time SeriesHaibin Cheng, Pang-Ning Tan, Christopher Potter, Steven KloosterHaibin Kloosterpp.413 - 424Chapter DOI:https://doi.org/10.1137/1.9781611972795.36PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Anomaly detection multivariate time series is an important data...
Malware is one of the most damaging security threats facing Internet today. Despite burgeoning literature, accurate detection malware remains an elusive and challenging endeavor due to increasing usage payload encryption sophisticated obfuscation methods. Also, large variety classes coupled with their rapid proliferation polymorphic capabilities imperfections real-world data (noise, missing values, etc) continue hinder use more algorithms. This paper presents a novel machine learning based...