Data Mining for Intrusion Detection: from Outliers to True - HAL
... seen before (and is thus considered as abnormal). Considering the large amount of new usage patterns emerging in the Information Systems, even a weak percent of false positive will give a very large amount of spurious alarms that would be overwhelming for the analyst. Therefore, the goal of this pap ...
... seen before (and is thus considered as abnormal). Considering the large amount of new usage patterns emerging in the Information Systems, even a weak percent of false positive will give a very large amount of spurious alarms that would be overwhelming for the analyst. Therefore, the goal of this pap ...
Finding density-based subspace clusters in graphs with feature
... Either both clusters are merged or some objects are lost. In Fig. 1b an extract of the corresponding network structure is shown. Since the restrictive quasi-clique property would only assign a low density to this cluster, the cluster would probably be split by the model of Günnemann et al. (2010). I ...
... Either both clusters are merged or some objects are lost. In Fig. 1b an extract of the corresponding network structure is shown. Since the restrictive quasi-clique property would only assign a low density to this cluster, the cluster would probably be split by the model of Günnemann et al. (2010). I ...
as a PDF
... New approach like data mining is required for quality prediction and control to keep pace with increased complexity of manufacturing. Data Mining is the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns. [4] The domain knowledge is used to gui ...
... New approach like data mining is required for quality prediction and control to keep pace with increased complexity of manufacturing. Data Mining is the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns. [4] The domain knowledge is used to gui ...
Introduction to Similarity Assessment and Clustering
... There is a separate “quality” function that measures the “goodness” of a cluster. The definitions of similarity functions are usually very different for interval-scaled, boolean, categorical, ordinal and ratio-scaled variables. Weights should be associated with different variables based on applicati ...
... There is a separate “quality” function that measures the “goodness” of a cluster. The definitions of similarity functions are usually very different for interval-scaled, boolean, categorical, ordinal and ratio-scaled variables. Weights should be associated with different variables based on applicati ...
Using an Ontology-based Approach for Geospatial Clustering
... Because OWL can formally represent the meaning of the domain terminology and it allows performing useful reasoning tasks on these documents, we used it as the language to represent the ontology. We use the OWL Plugin [14] of Protégé-2000 to construct the spatial clustering ontology. The current onto ...
... Because OWL can formally represent the meaning of the domain terminology and it allows performing useful reasoning tasks on these documents, we used it as the language to represent the ontology. We use the OWL Plugin [14] of Protégé-2000 to construct the spatial clustering ontology. The current onto ...
Multi-Agent Clustering - Computer Science Intranet
... The agents within our framework need to be able to communicate to carry out their tasks. JADE provides a communication mechanism that makes use of the FIPA ACL performatives [9]. However, as has been discussed previously in [19], the FIPA ACL has limited applicability to dialogues not involving purc ...
... The agents within our framework need to be able to communicate to carry out their tasks. JADE provides a communication mechanism that makes use of the FIPA ACL performatives [9]. However, as has been discussed previously in [19], the FIPA ACL has limited applicability to dialogues not involving purc ...
Intro PDB - University of Louisiana at Lafayette
... clustering obtains a partition of the objects into clusters such that the objects in a cluster are more similar to each other than to objects in different clusters. K-means, and K-mediod methods determine K cluster representatives and assign each object to the cluster with its representative closest ...
... clustering obtains a partition of the objects into clusters such that the objects in a cluster are more similar to each other than to objects in different clusters. K-means, and K-mediod methods determine K cluster representatives and assign each object to the cluster with its representative closest ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.