Data Mining for Intrusion Detection: from Outliers to True
... false positive will give a very large amount of spurious alarms that would be overwhelming for the analyst. Therefore, the goal of this paper is to propose an intrusion detection algorithm that is based on the analysis of usage data coming from multiple partners in order to reduce the number of fals ...
... false positive will give a very large amount of spurious alarms that would be overwhelming for the analyst. Therefore, the goal of this paper is to propose an intrusion detection algorithm that is based on the analysis of usage data coming from multiple partners in order to reduce the number of fals ...
Version2 - School of Computer Science
... analysis and mining on software engineering data. Our results of the investigation have shown the existing research on clustering SE data does not provide comparative or empirical analysis information or heuristics on which clustering techniques can help generate the clusters numbers automatically o ...
... analysis and mining on software engineering data. Our results of the investigation have shown the existing research on clustering SE data does not provide comparative or empirical analysis information or heuristics on which clustering techniques can help generate the clusters numbers automatically o ...
Breast Cancer Prediction using Data Mining Techniques
... Abstract—Cancer is the most central element for death around the world. In 2012, there are 8.2 million cancer demise worldwide and future anticipated that would have 13 million death by growth in 2030.The earlier forecast and location of tumor can be useful in curing the illness. So the examination ...
... Abstract—Cancer is the most central element for death around the world. In 2012, there are 8.2 million cancer demise worldwide and future anticipated that would have 13 million death by growth in 2030.The earlier forecast and location of tumor can be useful in curing the illness. So the examination ...
Linearly Decreasing Weight Particle Swarm Optimization with
... inherent structures that presents in the objects. The purpose of cluster analysis is to classify the clusters into subsets. In this context, each subset and its particular problem have certain meanings. More specifically, a set of patterns usually are vectors in a multi-dimensional space that are gr ...
... inherent structures that presents in the objects. The purpose of cluster analysis is to classify the clusters into subsets. In this context, each subset and its particular problem have certain meanings. More specifically, a set of patterns usually are vectors in a multi-dimensional space that are gr ...
A framework for spatio-temporal clustering from mobile phone data
... moving clusters identification and trajectory clustering. Moving clusters refer to a set of objects that move close to each other for a long time interval [17], while trajectory clustering focuses on classification and regrouping of multiple trajectories based on their shapes and other features. Obj ...
... moving clusters identification and trajectory clustering. Moving clusters refer to a set of objects that move close to each other for a long time interval [17], while trajectory clustering focuses on classification and regrouping of multiple trajectories based on their shapes and other features. Obj ...
An Efficient Approach for Test Suite Reduction using Density based
... clustering algorithm and tools used for testing and mining test cases. In section 4, research methodology is presented. In section 5, experimental results are shown. In section 6, performance has been evaluated. In section 7, concluding remarks and future work to be done in this area has been discus ...
... clustering algorithm and tools used for testing and mining test cases. In section 4, research methodology is presented. In section 5, experimental results are shown. In section 6, performance has been evaluated. In section 7, concluding remarks and future work to be done in this area has been discus ...
Adaptive Privacy-Preserving Visualization Using Parallel Coordinates
... axis pairs. This has two advantages: a) the clustering algorithm takes local properties between adjacent axis pairs into account, independent of the other axes, as a result of which cluster sizes are optimized and b) as pointed out by Li et al. [19] in parallel coordinates users are ultimately inter ...
... axis pairs. This has two advantages: a) the clustering algorithm takes local properties between adjacent axis pairs into account, independent of the other axes, as a result of which cluster sizes are optimized and b) as pointed out by Li et al. [19] in parallel coordinates users are ultimately inter ...
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.