The Experts below are selected from a list of 7308 Experts worldwide ranked by ideXlab platform
William Perrizo - One of the best experts on this subject based on the ideXlab platform.
-
a vertical distance based Outlier Detection Method with local pruning
Conference on Information and Knowledge Management, 2004Co-Authors: Dongmei Ren, William Perrizo, Imad Rahal, Kirk ScottAbstract:"One person's noise is another person's signal". Outlier Detection is used to clean up datasets and also to discover useful anomalies, such as criminal activities in electronic commerce, computer intrusion attacks, terrorist threats, agricultural pest infestations, etc. Thus, Outlier Detection is critically important in the information-based society. This paper focuses on finding Outliers in large datasets using distance-based Methods. First, to speedup Outlier Detections, we revise Knorr and Ng's distance-based Outlier definition; second, a vertical data structure, instead of traditional horizontal structures, is adopted to facilitate efficient Outlier Detection further. We tested our Methods against national hockey league dataset and show an order of magnitude of speed improvement compared to the contemporary distance-based Outlier Detection approaches.
-
rdf a density based Outlier Detection Method using vertical data representation
International Conference on Data Mining, 2004Co-Authors: Dongmei Ren, Baoying Wang, William PerrizoAbstract:Outlier Detection can lead to discovering unexpected and interesting knowledge, which is critical important to some areas such as monitoring of criminal activities in electronic commerce, credit card fraud, etc. In this paper, we developed an efficient density-based Outlier Detection Method for large datasets. Our contributions are: a) we introduce a relative density factor (RDF); b) based on RDF, we propose an RDF-based Outlier Detection Method which can efficiently prune the data points which are deep in clusters, and detect Outliers only within the remaining small subset of the data; c) the performance of our Method is further improved by means of a vertical data representation, P-trees. We tested our Method with NHL and NBA data. Our Method shows an order of magnitude speed improvement compared to the contemporary approaches.
-
CAINE - A P-Tree based Outlier Detection Method.
2004Co-Authors: Dongmei Ren, Baoying Wang, William PerrizoAbstract:Outlier Detection can discover unexpected and interesting knowledge, which is often important to the information society. In this paper, we developed an efficient density-based Outlier Detection Method for large datasets. In our Method, Outliers are efficiently detected from the candidate data subsets which contain potential Outliers. Furthermore, the algorithm is implemented using a vertical data organization model, P-Tree, which speed up the algorithm significantly. We tested our Method with NHL data. Experiment shows that our Method has an order of magnitude speed improvement over the contemporary approaches.
-
ICDM - RDF: a density-based Outlier Detection Method using vertical data representation
Fourth IEEE International Conference on Data Mining (ICDM'04), 1Co-Authors: Dongmei Ren, Baoying Wang, William PerrizoAbstract:Outlier Detection can lead to discovering unexpected and interesting knowledge, which is critical important to some areas such as monitoring of criminal activities in electronic commerce, credit card fraud, etc. In this paper, we developed an efficient density-based Outlier Detection Method for large datasets. Our contributions are: a) we introduce a relative density factor (RDF); b) based on RDF, we propose an RDF-based Outlier Detection Method which can efficiently prune the data points which are deep in clusters, and detect Outliers only within the remaining small subset of the data; c) the performance of our Method is further improved by means of a vertical data representation, P-trees. We tested our Method with NHL and NBA data. Our Method shows an order of magnitude speed improvement compared to the contemporary approaches.
Dongmei Ren - One of the best experts on this subject based on the ideXlab platform.
-
a vertical distance based Outlier Detection Method with local pruning
Conference on Information and Knowledge Management, 2004Co-Authors: Dongmei Ren, William Perrizo, Imad Rahal, Kirk ScottAbstract:"One person's noise is another person's signal". Outlier Detection is used to clean up datasets and also to discover useful anomalies, such as criminal activities in electronic commerce, computer intrusion attacks, terrorist threats, agricultural pest infestations, etc. Thus, Outlier Detection is critically important in the information-based society. This paper focuses on finding Outliers in large datasets using distance-based Methods. First, to speedup Outlier Detections, we revise Knorr and Ng's distance-based Outlier definition; second, a vertical data structure, instead of traditional horizontal structures, is adopted to facilitate efficient Outlier Detection further. We tested our Methods against national hockey league dataset and show an order of magnitude of speed improvement compared to the contemporary distance-based Outlier Detection approaches.
-
rdf a density based Outlier Detection Method using vertical data representation
International Conference on Data Mining, 2004Co-Authors: Dongmei Ren, Baoying Wang, William PerrizoAbstract:Outlier Detection can lead to discovering unexpected and interesting knowledge, which is critical important to some areas such as monitoring of criminal activities in electronic commerce, credit card fraud, etc. In this paper, we developed an efficient density-based Outlier Detection Method for large datasets. Our contributions are: a) we introduce a relative density factor (RDF); b) based on RDF, we propose an RDF-based Outlier Detection Method which can efficiently prune the data points which are deep in clusters, and detect Outliers only within the remaining small subset of the data; c) the performance of our Method is further improved by means of a vertical data representation, P-trees. We tested our Method with NHL and NBA data. Our Method shows an order of magnitude speed improvement compared to the contemporary approaches.
-
CAINE - A P-Tree based Outlier Detection Method.
2004Co-Authors: Dongmei Ren, Baoying Wang, William PerrizoAbstract:Outlier Detection can discover unexpected and interesting knowledge, which is often important to the information society. In this paper, we developed an efficient density-based Outlier Detection Method for large datasets. In our Method, Outliers are efficiently detected from the candidate data subsets which contain potential Outliers. Furthermore, the algorithm is implemented using a vertical data organization model, P-Tree, which speed up the algorithm significantly. We tested our Method with NHL data. Experiment shows that our Method has an order of magnitude speed improvement over the contemporary approaches.
-
ICDM - RDF: a density-based Outlier Detection Method using vertical data representation
Fourth IEEE International Conference on Data Mining (ICDM'04), 1Co-Authors: Dongmei Ren, Baoying Wang, William PerrizoAbstract:Outlier Detection can lead to discovering unexpected and interesting knowledge, which is critical important to some areas such as monitoring of criminal activities in electronic commerce, credit card fraud, etc. In this paper, we developed an efficient density-based Outlier Detection Method for large datasets. Our contributions are: a) we introduce a relative density factor (RDF); b) based on RDF, we propose an RDF-based Outlier Detection Method which can efficiently prune the data points which are deep in clusters, and detect Outliers only within the remaining small subset of the data; c) the performance of our Method is further improved by means of a vertical data representation, P-trees. We tested our Method with NHL and NBA data. Our Method shows an order of magnitude speed improvement compared to the contemporary approaches.
Baoying Wang - One of the best experts on this subject based on the ideXlab platform.
-
rdf a density based Outlier Detection Method using vertical data representation
International Conference on Data Mining, 2004Co-Authors: Dongmei Ren, Baoying Wang, William PerrizoAbstract:Outlier Detection can lead to discovering unexpected and interesting knowledge, which is critical important to some areas such as monitoring of criminal activities in electronic commerce, credit card fraud, etc. In this paper, we developed an efficient density-based Outlier Detection Method for large datasets. Our contributions are: a) we introduce a relative density factor (RDF); b) based on RDF, we propose an RDF-based Outlier Detection Method which can efficiently prune the data points which are deep in clusters, and detect Outliers only within the remaining small subset of the data; c) the performance of our Method is further improved by means of a vertical data representation, P-trees. We tested our Method with NHL and NBA data. Our Method shows an order of magnitude speed improvement compared to the contemporary approaches.
-
CAINE - A P-Tree based Outlier Detection Method.
2004Co-Authors: Dongmei Ren, Baoying Wang, William PerrizoAbstract:Outlier Detection can discover unexpected and interesting knowledge, which is often important to the information society. In this paper, we developed an efficient density-based Outlier Detection Method for large datasets. In our Method, Outliers are efficiently detected from the candidate data subsets which contain potential Outliers. Furthermore, the algorithm is implemented using a vertical data organization model, P-Tree, which speed up the algorithm significantly. We tested our Method with NHL data. Experiment shows that our Method has an order of magnitude speed improvement over the contemporary approaches.
-
ICDM - RDF: a density-based Outlier Detection Method using vertical data representation
Fourth IEEE International Conference on Data Mining (ICDM'04), 1Co-Authors: Dongmei Ren, Baoying Wang, William PerrizoAbstract:Outlier Detection can lead to discovering unexpected and interesting knowledge, which is critical important to some areas such as monitoring of criminal activities in electronic commerce, credit card fraud, etc. In this paper, we developed an efficient density-based Outlier Detection Method for large datasets. Our contributions are: a) we introduce a relative density factor (RDF); b) based on RDF, we propose an RDF-based Outlier Detection Method which can efficiently prune the data points which are deep in clusters, and detect Outliers only within the remaining small subset of the data; c) the performance of our Method is further improved by means of a vertical data representation, P-trees. We tested our Method with NHL and NBA data. Our Method shows an order of magnitude speed improvement compared to the contemporary approaches.
Hichem Snoussi - One of the best experts on this subject based on the ideXlab platform.
-
SoftCOM - One class Outlier Detection Method in wireless sensor networks: Comparative study
2016 24th International Conference on Software Telecommunications and Computer Networks (SoftCOM), 2016Co-Authors: Oussama Ghorbel, Mohamed Abid, Abdulfattah M. Obeid, Hichem SnoussiAbstract:Recent advances in communication technology have enable the emergency of new types of wireless networks: Wireless Sensor Networks (WSN). It consists of a huge number of tiny and low cost devices with sensing and communication capabilities. They are emerging recently as a key solution to monitor remote environments and concern a wide range of applications from the environmental and military surveillance to home automation. However, these models are not suitable for the energy constrained WSNs because they assumed the whole data is available in a central location for further analysis. In this paper, we present a comparative study between Centralised and Distributed One-class Outliers Detection Classifier (COODC & DOODC) based on Mahalanobis Kernel used for Outlier Detection in wireless sensor networks (WSNs). For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or Outliers. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. On account of the attractive capability, KPCA-based Methods have been extensively investigated, and have showed excellent performance. Within this setting, we propose Kernel Principal Component Analysis based Mahalanobis kernel as a new Outlier Detection Method using Mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate Outlier points from normal pattern of data distribution. The use of Distributed One-class Outliers Detection Classifier based on Mahalanobis Kernel on real word data obtained from Intel Berkeley are reported showing that the proposed Method performs better in finding Outliers in wireless sensor networks when compared to the Centralised One-class Outliers Detection Classifier (COODC).
-
Fast and Efficient Outlier Detection Method in Wireless Sensor Networks
IEEE Sensors Journal, 2015Co-Authors: Oussama Ghorbel, Hichem Snoussi, Walid Ayedi, Mohamed AbidAbstract:Outlier Detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as one-class classification, in which a model is constructed to describe normal training data. In wireless sensor networks (WSNs), the Outlier Detection process is a necessary step in building sensor network systems to assure data quality for perfect decision making. For this case, the task amounts to create a useful model based on kernel principal component analysis (KPCA) to recognize data as normal or Outliers. Recently, KPCA has used for nonlinear case which can extract higher order statistics. KPCA mapping the data onto another feature space and using nonlinear function. On account of the attractive capability, KPCA-based Methods have been extensively investigated, and it have showed excellent performance. Within this setting, we propose KPCA-based Mahalanobis kernel as a new Outlier Detection Method using Mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate Outlier points from normal pattern of data distribution. The use of KPCA-based Mahalanobis kernel on real-word data obtained from Intel Berkeley are reported showing that the proposed Method performs better in finding Outliers in WSNs when compared with the original reconstruction error-based variant and the one-class support vector machine Detection approach. All computation are done in the original space, thus saving computing time using Mahalanobis kernel.
-
Kernel Principal Subspace Based Outlier Detection Method in Wireless Sensor Networks
2014Co-Authors: Oussama Ghorbel, Mohamed Abid, Hichem SnoussiAbstract:An emerging class of Wireless Sensor Networks (WSNs) applications involves the acquisition of large amounts of sensory data from battery-powered, low computation and low memory wireless sensor nodes. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to creating a useful model based on KPCA to recognize data as normal or Outliers. Over the last few years, Kernel Principal Component Analysis (KPCA) has found several applications in Outlier Detection. Within this setting, we propose a new Outlier Detection Method based on Kernel Principal Component Analysis (KPCA) using mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate Outlier points from normal pattern of data distribution. The use of KPCA based mahalanobis kernel on real word data obtained from Intel Berkeley are reported showing that the proposed Method performs better in finding Outliers in wireless sensor networks.
-
AINA Workshops - Kernel Principal Subspace Based Outlier Detection Method in Wireless Sensor Networks
2014 28th International Conference on Advanced Information Networking and Applications Workshops, 2014Co-Authors: Oussama Ghorbel, Mohamed Abid, Hichem SnoussiAbstract:An emerging class of Wireless Sensor Networks (WSNs) applications involves the acquisition of large amounts of sensory data from battery-powered, low computation and low memory wireless sensor nodes. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to creating a useful model based on KPCA to recognize data as normal or Outliers. Over the last few years, Kernel Principal Component Analysis (KPCA) has found several applications in Outlier Detection. Within this setting, we propose a new Outlier Detection Method based on Kernel Principal Component Analysis (KPCA) using mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate Outlier points from normal pattern of data distribution. The use of KPCA based mahalanobis kernel on real word data obtained from Intel Berkeley are reported showing that the proposed Method performs better in finding Outliers in wireless sensor networks.
Oussama Ghorbel - One of the best experts on this subject based on the ideXlab platform.
-
classification data using Outlier Detection Method in wireless sensor networks
International Conference on Wireless Communications and Mobile Computing, 2017Co-Authors: Oussama Ghorbel, A Ayadi, Kais Loukil, Mohamed S Bensaleh, Mohamed AbidAbstract:For classification data, we use Wireless sensor networks (WSNs) as hardware for collecting data from harsh environments and controlling important events in phenomena. To evaluate the quality of a sensor and its network, we use the accuracy of sensor readings as surely one of the most important measures. Therefore, for anomalous measurement, real time Detection is required to guarantee the quality of data collected by these networks. In this case, the task amounts to create a useful model based on KPCA to recognize data as normal or Outliers. On account of the attractive capability, KPCA-based Methods have been extensively investigated, and have shown excellent performance. So, to extract relevant feature for classification and to prevent from the events, we use KPCA based on Mahalanobis kernel as a preprocessing step. In the original space, the totality of computation is done thus saving computing time. Then the classification was done on real Intel Berkeley data collecting from urban area. Compared to a standard KPCA, the results show that our Method are specially designed to be used in the field of wireless sensor networks (WSNs).
-
SoftCOM - One class Outlier Detection Method in wireless sensor networks: Comparative study
2016 24th International Conference on Software Telecommunications and Computer Networks (SoftCOM), 2016Co-Authors: Oussama Ghorbel, Mohamed Abid, Abdulfattah M. Obeid, Hichem SnoussiAbstract:Recent advances in communication technology have enable the emergency of new types of wireless networks: Wireless Sensor Networks (WSN). It consists of a huge number of tiny and low cost devices with sensing and communication capabilities. They are emerging recently as a key solution to monitor remote environments and concern a wide range of applications from the environmental and military surveillance to home automation. However, these models are not suitable for the energy constrained WSNs because they assumed the whole data is available in a central location for further analysis. In this paper, we present a comparative study between Centralised and Distributed One-class Outliers Detection Classifier (COODC & DOODC) based on Mahalanobis Kernel used for Outlier Detection in wireless sensor networks (WSNs). For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or Outliers. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. On account of the attractive capability, KPCA-based Methods have been extensively investigated, and have showed excellent performance. Within this setting, we propose Kernel Principal Component Analysis based Mahalanobis kernel as a new Outlier Detection Method using Mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate Outlier points from normal pattern of data distribution. The use of Distributed One-class Outliers Detection Classifier based on Mahalanobis Kernel on real word data obtained from Intel Berkeley are reported showing that the proposed Method performs better in finding Outliers in wireless sensor networks when compared to the Centralised One-class Outliers Detection Classifier (COODC).
-
Fast and Efficient Outlier Detection Method in Wireless Sensor Networks
IEEE Sensors Journal, 2015Co-Authors: Oussama Ghorbel, Hichem Snoussi, Walid Ayedi, Mohamed AbidAbstract:Outlier Detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as one-class classification, in which a model is constructed to describe normal training data. In wireless sensor networks (WSNs), the Outlier Detection process is a necessary step in building sensor network systems to assure data quality for perfect decision making. For this case, the task amounts to create a useful model based on kernel principal component analysis (KPCA) to recognize data as normal or Outliers. Recently, KPCA has used for nonlinear case which can extract higher order statistics. KPCA mapping the data onto another feature space and using nonlinear function. On account of the attractive capability, KPCA-based Methods have been extensively investigated, and it have showed excellent performance. Within this setting, we propose KPCA-based Mahalanobis kernel as a new Outlier Detection Method using Mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate Outlier points from normal pattern of data distribution. The use of KPCA-based Mahalanobis kernel on real-word data obtained from Intel Berkeley are reported showing that the proposed Method performs better in finding Outliers in WSNs when compared with the original reconstruction error-based variant and the one-class support vector machine Detection approach. All computation are done in the original space, thus saving computing time using Mahalanobis kernel.
-
Kernel Principal Subspace Based Outlier Detection Method in Wireless Sensor Networks
2014Co-Authors: Oussama Ghorbel, Mohamed Abid, Hichem SnoussiAbstract:An emerging class of Wireless Sensor Networks (WSNs) applications involves the acquisition of large amounts of sensory data from battery-powered, low computation and low memory wireless sensor nodes. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to creating a useful model based on KPCA to recognize data as normal or Outliers. Over the last few years, Kernel Principal Component Analysis (KPCA) has found several applications in Outlier Detection. Within this setting, we propose a new Outlier Detection Method based on Kernel Principal Component Analysis (KPCA) using mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate Outlier points from normal pattern of data distribution. The use of KPCA based mahalanobis kernel on real word data obtained from Intel Berkeley are reported showing that the proposed Method performs better in finding Outliers in wireless sensor networks.
-
AINA Workshops - Kernel Principal Subspace Based Outlier Detection Method in Wireless Sensor Networks
2014 28th International Conference on Advanced Information Networking and Applications Workshops, 2014Co-Authors: Oussama Ghorbel, Mohamed Abid, Hichem SnoussiAbstract:An emerging class of Wireless Sensor Networks (WSNs) applications involves the acquisition of large amounts of sensory data from battery-powered, low computation and low memory wireless sensor nodes. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to creating a useful model based on KPCA to recognize data as normal or Outliers. Over the last few years, Kernel Principal Component Analysis (KPCA) has found several applications in Outlier Detection. Within this setting, we propose a new Outlier Detection Method based on Kernel Principal Component Analysis (KPCA) using mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate Outlier points from normal pattern of data distribution. The use of KPCA based mahalanobis kernel on real word data obtained from Intel Berkeley are reported showing that the proposed Method performs better in finding Outliers in wireless sensor networks.
