Browsing by Author "WuLi, Wei"
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Item Extending Data Mining for Spatial Applications: A Case Study in Predicting Nest Locations(2000-04-18) Chawla, Sanjay; Shekhar, Shashi; WuLi, Wei; Ozesmi, UygarSpatial data mining is a process to discover interesting and potentially useful spatial patterns embedded in spatial databases. Efficient tools for extracting information from spatial data sets can be of importance to organizations which own, generate and manage large geo-spatial data sets. The current approach towards solving spatial data mining problems is to use classical data mining tools after "materializing" spatial relationships and assuming independence between different data points. However, classical data mining methods often perform poorly on spatial data sets which have high spatial auto-correlation. In this paper we will review spatial statistical techniques which can effectively model the notion of spatial-autocorrelation and apply it to the problem of predicting bird nest locations in a marshland.Item Maximal Independent Set and Minimum Connected Dominating Set in Unit Disk Graphs(2004-12-13) WuLi, Wei; Du, Hongwei; Jia, Xiaohua; Li, Yingshu; Huang, Scott C.-H.; Du, Ding-ZhuIn ad hoc wireless networks, the connected dominating set can be used as a virtual backbone to improve the performance. Many constructions for approximating the minimum connected dominating set are based on construction of maximal independent set. The relation between the size mis(G) of a maximum independent set and the size cds(G) of minimum connected dominating set in the same graph G plays an important role in establishing the performance ratio of those approximation algorithms. Previously, it is known that mis(G)<=4*cds(G)+1 for all unit disk graph G. In this paper, we improve it by showing mis(G)<=3.8*cds(G)+1.2.Item Spatial Contextual Classification and Prediction Models for Mining Geospatial Data(2002-02-14) Shekhar, Shashi; Schrater, Paul; Vatsavai, Ranga R.; WuLi, Wei; Chawla, SanjayModeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov Random Fields (MRFs) is a popular model for incorporating spatial context into image segmentation and land-use classification problems. The spatial autoregression model (SAR), which is an extension of the classical regression model for incorporating spatial dependence, is popular for prediction and classification of spatial data in regional economics, natural resources, and ecological studies. There is little literature comparing these alternative approaches to facilitate the exchange of ideas (e.g., solution procedures). We argue that the SAR model makes more restrictive assumptions about the distribution of feature values and class boundaries than MRF. The relationship between SAR and MRF is analogousto the relationship between regression and Bayesian classifiers. This paper provides comparisons between the two models using a probabilistic and an experimental framework. Keywords: Spatial Context, Spatial Data Mining, Markov Random Fields, Spatial Autoregression.