S. Shekhar, C.T. Lu, and P. Zhang. Detecting Graph-Based Spatial Outlier: Algorithms and Applications(A Summary of Results). In Proc. of the Seventh ACM-SIGKDD Int'l Conference on Knowledge Discovery and Data Mining, Aug 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....for tools that can automatically transform the processed data into useful information and knowledge. Data mining allows organizations and companies to extract useful information from the vast amount of data they have gathered, thus helping them make more effective decisions. Spatial data mining [18, 19, 26, 25, 5], a subfield of data mining, is concerned with the discovery of interesting and This work is partially supported by the Army High Performance Computing Research Center under the auspices of the Department of the Army, Army Research Laboratory cooperative agreement number DAAD19 012 0014,the ....

....each iteration and dominates the I O cost of the entire algorithm. The storage of the data set should support the I O efficient computation of this operation. The detailed discussions about I O efficient computations, the choices for storage structure, and experimental comparisons are available at [26]. Algorithm 2. Route Outlier Detection(ROD) Algorithm # is the distance function in #; ## is the confidence interval; # # ,# # ) are mean and standard deviation calculated in TPC; ## is the set of node in a route; Output: Outlier Set. for(i=1;i # #### ;i )# ##### ####=0; ....

S. Shekhar, C. Lu, and P. Zhang. Detecting Graph-Based Spatial Outlier: Algorithms and Applications(A Summary of Results). In Computer Science & Engineering Department, UMN, Technical Report 01-014, 2001.

....for tools that can automatically transform the processed data into useful information and knowledge. Data mining allows organizations and companies to extract useful information from the vast amount of data they have gathered, thus helping them make more effective decisions. Spatial data mining [18, 19, 26, 25, 5], a subfield of data mining, is concerned with the discovery of interesting and This work is partially supported by the Army High Performance Computing Research Center under the auspices of the Department of the Army, Army Research Laboratory cooperative agreement number DAAD19 012 0014,the ....

....each iteration and dominates the I O cost of the entire algorithm. The storage of the data set should support the I O efficient computation of this operation. The detailed discussions about I O efficient computations, the choices for storage structure, and experimental comparisons are available at [26]. Algorithm 2. Route Outlier Detection(ROD) Algorithm F is the distance function in S; CI is the confidence interval; s , s ) are mean and standard deviation calculated in TPC; RN is the set of node in a route; Output: Outlier Set. for(i=1;i jRN j ;i )f AvgDist = Accum ....

S. Shekhar, C. Lu, and P. Zhang. Detecting Graph-Based Spatial Outlier: Algorithms and Applications(A Summary of Results). In Computer Science & Engineering Department, UMN, Technical Report 01-014, 2001.

....8 9, 2002, McLean, Virginia, USA. Copyright 2002 ACM 1 58113 XXX X 02 0011 . 5.00. Keywords vector map compression, clustering, dictionary design 1. INTRODUCTION Mobile computing devices, e.g. personal digital assistants (PDA) and in car navigation units, require access to spatial datasets [5, 10, 11, 13, 14] such as vector maps for locationbased services. An example of a query would be Where is the nearest gas station Vector maps, e.g. road maps, consist of a collection of points (e.g. road intersections) line strings (e.g. center line of road segments connecting intersections) and polygons ....

S. Shekhar, C. Lu, and P. Zhang. Detecting Graph-based Spatial Outliers: Algorithms and Applications. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.

.... of which does not necessarily re ect the position or the policy of the government, and no ocial endorsement should be inferred 1 Introduction Widespread use of spatial databases [10, 24, 25, 36] is leading to an increasing interest in mining interesting and useful but implicit spatial patterns [9, 16, 20, 23, 32, 26, 28, 5, 29, 35]. For example, E services are growing along with mobile computing infrastructures such as PDAs and celluar phones. Finding E services frequently located together is of interest to businesses that want to conduct location sensitive market promotions such as promoting a taxi service for customers ....

S. Shekhar, C.T. Lu, and P. Zhang. Detecting Graph-based Spatial Outliers: Algorithms and Applications. The Seventh ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2001.

....performance study shows that our method is both e ective and ecient for large spatial databases. Keywords spatial data mining, con dent co location rules 1. INTRODUCTION Spatial data mining becomes more interesting and important as more spatial data have been accumulated in spatial databases [9, 11, 12, 4, 6, 7]. Spatial patterns are of great values in many applications. For example, in mobile computing, to provide location sensitive promotions, it is demanding to nd services requested frequently and located together from mobile devices such as PDAs. Mining spatial co location patterns [10, 8, 3] is an ....

S. Shekhar, C. Lu, and P. Zhang. Detecting Graph-based Spatial Outliers: Algorithms and Applications. KDD'01.

....distribution of attribute data. Finally, they seldom provide the confidence measure of the discovered outliers. In this following subsection, we describe a general framework for detecting spatial outliers in a spatial data set with an underlying graph structure. The detailed work can be found in [SLZ01] 22 Choice of Spatial Statistic. For spatial statistics, several parameters should be pre determined before running the spatial outlier test. First, the neighborhood must be selected, based on a fixed cardinality or a fixed graph distance or a fixed Euclidean distance. Second, the aggregate ....

S. Shekhar, C.T. Lu, and P Zhang. Detecting Graph-based Spatial Outliers: Algorithms and Applications . In Department of Computer Science Techinical Report TR 01-014, 28 University of Minnesota: http://tiberius.cs.umn.edu/techreports /listing/, 2001.

....distribution of attribute data. Finally, they seldom provide the confidence measure of the discovered outliers. In this following subsection, we describe a general framework for detecting spatial outliers in a spatial data set with an underlying graph structure. The detailed work can be found in [SLZ01] Choice of Spatial Statistic. For spatial statistics, several parameters should be pre determined before running the spatial outlier test. First, the neighborhood must be selected, based on a fixed cardinality or a fixed graph distance or a fixed Euclidean distance. Second, the aggregate ....

S. Shekhar, C.T. Lu, and P Zhang. Detecting Graph-based Spatial Outliers: Algorithms and Applications . In Department of Computer Science Techinical Report TR 01-014, University of Minnesota: http://tiberius.cs.umn.edu/techreports /listing/, 2001.

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S. Shekhar, C.T. Lu, and P. Zhang. Detecting Graph-Based Spatial Outlier: Algorithms and Applications(A Summary of Results). In Proc. of the Seventh ACM-SIGKDD Int'l Conference on Knowledge Discovery and Data Mining, Aug 2001.

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S. Shekhar, C.-T. Lu, and P. Zhang. Detecting graph-based spatial outliers: Algorithms and applications (a summary of results). In Proc. of KDD'2001, 2001.

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