Protocol reverse engineering is very important for information security. In the complex wireless network environment, in order to separate binary data frames for subsequent reverse protocol analysis, this paper propos...
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ISBN:
(纸本)9781510804289
Protocol reverse engineering is very important for information security. In the complex wireless network environment, in order to separate binary data frames for subsequent reverse protocol analysis, this paper proposes a frame cluster system designed for binary frames using complex protocol stacks. It first uses AC algorithm to get the frequent characteristics of the binary frames, then creatively uses the Apriori algorithm to explore the relationship between these characteristics and the 4-step pruning process to choose the most important characteristics, and finally uses the selected characteristics and their relationships, through the kmeans algorithm to cluster the frames. Experiments show that the result is good, and if the protocol type field exist, it is possible to distinguish the layered relationships between different clusters.
Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. Aiming at the ...
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Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. Aiming at the shortages of the existing K-means algorithm of data-mining for customer classification, a new online trading customer classification algorithm is advanced based on combination of the K-means SelfOrganizing Feature Map(SOM) algorithms. Firstly, based on consumer characteristics and behaviors analysis, the paper designs 21 customer classification indicators including customer characteristics type variables and customer behaviors type variables. Secondly, the limitation of K-means algorithm is analyzed;Third, SOM & K-means Combination-Based customer Classification algorithms is advanced to overcome the shortage of the K-means classification algorithm and takes advantage of powerful classification ability of the algorithm to classify online trading customer. Finally the experimental results verify that the new algorithm can improve effectiveness and validity of customer classification when used for classifying network trading customers practically.
Diabetic retinopathy is a major cause of blindness. Earliest signs of diabetic retinopathy are damage to blood vessels in the eye and then the formation of lesions in the retina. This project presents a method for the...
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ISBN:
(纸本)9783642227196
Diabetic retinopathy is a major cause of blindness. Earliest signs of diabetic retinopathy are damage to blood vessels in the eye and then the formation of lesions in the retina. This project presents a method for the detection of abnormalities in the retina such as the exudates in retinopathy images using computational intelligence techniques. The color retinal images are segmented using fuzzy c-means clustering following color normalization and contrast enhancement. For classification of these segmented regions into exudates and nonexudates, a set of initial features such as color, size, edge strength, and texture are extracted. A genetic based algorithm is used to rank the features and identify the subset that gives the best classification results. Using a multilayer neural network classifier, the selected feature vectors are then classified.
In hierarchical cluster analysis dendrogram graphs are used to visualize how clusters are formed. Because each observation is displayed dendrograms are impractical when the data set is large. For non-hierarchical clus...
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In hierarchical cluster analysis dendrogram graphs are used to visualize how clusters are formed. Because each observation is displayed dendrograms are impractical when the data set is large. For non-hierarchical cluster algorithms (e.g. kmeans) a graph like the dendrogram does not exist. This paper discusses a graph named "clustergram" to examine how cluster members are assigned to clusters as the number of clusters increases. The clustergram can also give insight into algorithms. For example, it can easily be seen that the "single linkage" algorithm tends to form clusters that consist of just one observation. It is also useful in distinguishing between random and deterministic implementations of the kmeans algorithm. A data set related to asbestos claims and the Thailand Landmine Data are used throughout to illustrate the clustergram.
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