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The discovery of interesting regions in spatial datasets is an important data mining task. In particular, we are interested in identifying disjoint, contiguous regions that are unusual with respect to the distribution of a given class; i.e. a region that contains an unusually low or high number of instances of a particular class. This paper centers on the(More)
— With the constraints of network topologies and link capacities, achieving the optimal end-to-end throughput in data networks has been known as a fundamental but computationally hard problem. In this paper, we seek efficient solutions to the problem of achieving optimal throughput in data networks, with single or multiple uni-cast, multicast and broadcast(More)
The basic idea of traditional density estimation is to model the overall point density analytically as the sum of influence functions of data points. However, traditional density estimation techniques only consider the location of a point. Supervised density estimation techniques, on the other hand, additionally consider a variable of interest that is(More)
— With the constraints of network topologies and link capacities, achieving the optimal end-to-end throughput in data networks has been known as a fundamental but computationally hard problem. In this paper, we seek efficient solutions to the problem of achieving optimal throughput in data networks, with single or multiple unicast, multicast and broadcast(More)
This paper presents a novel region discovery framework geared towards finding scientifically interesting places in spatial datasets. We view region discovery as a clustering problem in which an externally given fitness function has to be maximized. The framework adapts four representative clustering algorithms, exemplifying prototype-based, grid-based,(More)
The basic idea of traditional density estimation is to model the overall point density analytically as the sum of influence functions of the data points. However, traditional density estimation techniques only consider the location of a point. Supervised density estimation techniques, on the other hand, additionally consider a variable of interest that is(More)
Feature-based hot spots are localized regions where the attributes of objects attain high values. There is considerable interest in automatic identification of feature-based hot spots. This paper approaches the problem of finding feature-based hot spots from a data mining perspective, and describes a method that relies on supervised clustering to produce a(More)
—In the Cloud computing community, the calculation of the reputation using the feedback of cloud customers is widely adopted to address the issue of trustworthiness of cloud services. Currently, most methods pursue a global reputation score essentially assuming that the value of a cloud service's reputation is the same for every consumer. However depending(More)