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Spatial data mining (SDM) is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. Being an important role of SDM, spatial clustering is to organize a set of spatial objects into groups (or clusters) such that objects in the same group are similar to each other and different from those in other groups. Spatial clustering has been...
Density-based spatial clustering algorithms can be used to filter out noise and outliers, and discover clusters of arbitrary shape, which are all relatively good algorithms. But when the problem of variable density distribution of spatial objects was taken in to consideration, the accuracy of clustering result can be largely affected by the distribution of spatial objects. Therefore, the strategy...
Intrusion detection is one of network security area of technology main research directions. Data mining technology was applied to network intrusion detection system (NIDS), may automatically discover the new pattern from the massive network data, to reduce the workload of the manual compilation intrusion behavior patterns and normal behavior patterns. This article reviewed the current intrusion detection...
Currently those algorithms to mine the alarm association rules are limited to the minimal support, so that they can only obtain the association rules among the frequently occurring alarms. This paper proposes a new mining algorithm based on spectral graph theory. The algorithms firstly sets up alarm association model with time series; Secondly, it regards alarms database as a high-dimensional structure...
In recent years, as the development of wireless sensor networks, people do some deep research on cluster-based protocol, most about the prolongation of the lifetime of WSN and decline of energy consumed by the sensors .This article analyses the cluster-heads generating algorithm among LEACH and presents improved approach that adjusting the nodes, threshold function. When non cluster-heads choose optimal...
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