Ickjai Lee

AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles

Wide spread clustering algorithms use the Euclidean distance to measure spatial proximity. However, obstacles in other GIS data-layers prevent traversing the straight path between two points. AUTOCLUST+ clusters points in the presence of obstacles based on Voronoi modeling and Delaunay Diagrams. The algorithm is free of usersupplied arguments and incorporates global and local variations. Thus, it detects high-quality clusters (clusters of arbitrary shapes, clusters of different densities, sparse clusters adjacent to high-density clusters, multiple bridges between clusters and closely located high-density clusters) without prior knowledge. Consequently, it successfully supports correlation analyses between layers (requiring high-quality clusters) and more general locational optimization problems in the presence of obstacles. All this within O(n log n+[m+R] log n) expected time, where n is the number of data points, m is the number of line-segments that determine the obstacles and R is the number of Delaunay edges intersecting some obstacles. A series of detailed performance evaluations illustrates the power of AUTOCLUST+ and confirms the virtues of our approach.

Argument free clustering for large spatial point-data sets via boundary extraction from Delaunay Diagram

Abstract Minimizing the need for user-specified arguments results in less costly Geographical Data Mining. For massive data sets, the need to find best-fit arguments in semi-automatic clustering is not the only concern, the manipulation of data to find arguments opposes the philosophy of “let the data speak for themselves” that underpins exploratory data analysis. Our new approach consists of effective and efficient methods for discovering cluster boundaries in point-data sets. Parameters are not specified by users. Rather, values for parameters are revealed from the proximity structures of Voronoi modeling, and thus, an algorithm, AUTOCLUST, calculates them from the Delunay Diagram. We detect clusters of different densities and sparse clusters near to high-density clusters. Multiple bridges linking clusters are identified and removed. All this within O ( n log n ) time, where n is the number of data points. We contrast AUTOCLUST with algorithms for clustering large georeferenced sets of points. These comparisons confirm the virtues of our approach.

Data structures and algorithms to support interactive spatial analysis using dynamic Voronoi diagrams

To support the need for interactive spatial analysis, it is often necessary to rethink the data structures and algorithms underpinning applications. This paper describes the development of an interactive environment in which a number of different Voronoi models of space can be manipulated together in real time to: (1) study their behaviour; (2) select appropriate models for specific analysis tasks; and (3) to examine how choice of one model over another will affect the interpretation of data. The paper studies six specific Voronoi diagram variants: the Ordinary Voronoi Diagram, the Farthest-point Voronoi Diagram, the Order-k Voronoi Diagram, the Ordered Order-k Voronoi Diagram, the kth Nearest-point Voronoi Diagram and the Multiplicatively Weighted Voronoi Diagram, and develops algorithms and data structures to store, rebuild and query these variants. From this, a generalised Voronoi data structure is proposed, from which specific Voronoi variants can be reconstructed dynamically as required. Algorithms for diagram reconstruction and for querying neighbourhood (topology or adjacency relations) of generator points and Voronoi regions are presented. An application program, developed on these ideas, is used to generate example results as proof of concept. It may be downloaded from a supporting website.

Exploration of geo-tagged photos through data mining approaches

With the development of web technique and social network sites human now can produce information, share with others online easily. Photo-sharing website, Flickr, stores huge number of photos where people upload and share their pictures. This research proposes a framework that is used to extract associative points-of-interest patterns from geo-tagged photos in Queensland, Australia, a popular tourist destination hosting the great Barrier Reef and tropical rain forest. This framework combines two popular data mining techniques: clustering for points-of-interest detection, and association rules mining for associative points-of-interest patterns. We report interesting experimental results and discuss findings.

Geographic Forwarding and Routing for Ad-hoc Wireless Network: A Survey

Routing for ad-hoc wireless network is challenging, and there is no single routing strategy to deal with the complex and dynamic nature of the ad-hoc network. Given the availability of the low cost of location-aware devices, geographic forwarding and routing provided opportunity for improving existing ad-hoc routing strategies. In this paper, geographic forwarding strategies and geographic routing protocols are reviewed; challenges of implementing geographic routing in ad-hoc network are identified and discussed. More research effort is called for improving the basic forwarding strategy to increase the efficiency of the geographic routing protocol to provide a practical solution addressing the issues related to routing around local minima.

Multi-Level Clustering and its Visualization for Exploratory Spatial Analysis

Exploratory spatial analysis is increasingly necessary as larger spatial data is managed in electro-magnetic media. We propose an exploratory method that reveals a robust clustering hierarchy from 2-D point data. Our approach uses the Delaunay diagram to incorporate spatial proximity. It does not require prior knowledge about the data set, nor does it require preconditions. Multi-level clusters are successfully discovered by this new method in only O(nlogn) time, where n is the size of the data set. The efficiency of our method allows us to construct and display a new type of tree graph that facilitates understanding of the complex hierarchy of clusters. We show that clustering methods adopting a raster-like or vector-like representation of proximity are not appropriate for spatial clustering. We conduct an experimental evaluation with synthetic data sets as well as real data sets to illustrate the robustness of our method.

A generic triangle-based data structure of the complete set of higher order Voronoi diagrams for emergency management

Abstract We introduce a generic Delaunay triangle-based data structure for geoinformation processing in disaster and emergency management. The data structure supports the complete set of higher order Voronoi diagrams (order- k ) Voronoi diagrams, ordered order- k Voronoi diagrams, and k -th nearest Voronoi diagrams for all ( k ) . It provides useful and insightful information for what-if nearest queries, what-if neighboring queries, what-if zoning queries, what-if facility locating queries and what-if routing queries to handle various scenarios in the four stages of emergency management (mitigation, preparedness, response and recovery). We also demonstrate how the complete set of higher order Voronoi diagrams can be used for each phase of emergency management in diverse geoinformatics environments.

Interactive analysis using Voronoi diagrams: Algorithms to support dynamic update from a generic triangle-based data structure

This paper describes a series of dynamic update methods th at can be applied to a family of Voronoi diagram types, so that changes can be updated incrementa lly, without the usual recourse to complete reconstruction of their underlying data structure. Mor e efficient incremental update methods are described for the ordinary Voronoi diagram, the farthest-point Voronoi diagram, the order-k Voronoi diagram, the ordered order- k Voronoi diagram and the k th nearest-point Voronoi diagram. A discussion is also given of one case where increme ntal update is not practical, that of the multiplicatively weighted Voronoi diagram. Update methods rely on a previously reported generic triangle-based data structure (Gahegan & Lee, 2000) f rom which local topology can be reconstructed following changes to the underlying pointset. An a pplication, which implements these ideas, is available for download via the Internet as proof of concept. Results show that the algorithmic complexity of dynamic update methods vary considerably according to the Voronoi type, but offer in all cases (except the multiplicativel y weighted Voronoi diagram) a substantial increase in performance, enabling Voronoi methods to addre ss larger pointsets and more complex modelling problems without suffering from efficiency problems.

Criteria on Proximity Graphs for Boundary Extraction and Spatial Clustering

Proximity and density information modeling of 2D point-data by Delaunay Diagrams has delivered a powerful exploratory and argument-free clustering algorithm [6] for geographical data mining [13]. The algorithm obtains cluster boundaries using a Short-Long criterion and detects non-convex clusters, high and low density clusters, clusters inside clusters and many other robust results. Moreover, its computation is linear in the size of the graph used. This paper demonstrates that the criterion remains effective for exploratory analysis and spatial data mining where other proximity graphs are used. It also establishes a hierarchy of the modeling power of several proximity graphs and presents how the argument free characteristic of the original algorithm can be traded for argument tuning. This enables higher than 2 dimensions by using linear size proximity graphs like k-nearest neighbors.

Mobile augmented reality based context-aware library management system

Mobile augmented reality has gained popularity in recent years due to the technological advances of smartphones and other mobile devices. One particular area in which mobile augmented reality is being used is library management. However, current mobile augmented reality solutions in this domain are lacking in context-awareness. It has been suggested in the literature that agent programming may be suitable at overcoming this problem, but little research has been conducted using modern mobile augmented reality applications with agents. This paper aims to bridge this gap through the development of an agent-based, mobile augmented reality prototype, titled Libagent. Libagent was subjected to five experiments to determine its suitability, efficiency, and accuracy for library management. The results of these experiments indicate that agent-based mobile augmented reality is a promising tool for context-aware library management.