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In this paper, we introduce an innovative fuzzy clustering model that includes some prior knowledge about the data. The prior knowledge is the data correlations expressed in a form of graph. Specifically, in this new model, we add a graph regularization term to the objective function of Fuzzy C-Mean (FCM) to fine-tune the final clustering result. By doing so, when we conduct fuzzy clustering to classify...
Prior knowledge has been considered as valuable information in many image processing techniques. In this paper, we take the original image itself as the prior and develop a new fuzzy clustering algorithm for image segmentation by adding a new term to the objective function of Fuzzy C-means. The new term comes from Guided Filter for its capability of suppressing noise and preserving edge information...
Fuzzy clustering methods are efficient tools for image segmentation. However, most of fuzzy clustering approaches are too sensitive to deal with the misclassification of pixels in image segmentation. In recent years, a variety of enhanced fuzzy clustering approaches have been proposed to obtain smoother results in noised image segmentation, but usually with less accurate edges in these results. To...
Analyzing massive network and security logs that record network events is crucial for diagnosing network anomalies in large-scale network environments. Extracting log message formats is an important and necessary step to achieve the goal. However, it is time-consuming and costly to automatically and efficiently extract log message formats from massive network and security logs of many different types,...
This paper propose three novel approaches for clustering, called LPK-means algorithm, LPK-medoids and LPMK-medoids, based on label propagation algorithm. LPK-means algorithm runs like k-means algorithm, meanwhile LPK-medoids algorithm and LPMK-medoids run like k-medoids algorithm. The three proposed algorithms partition clusters by label propagation. To evaluate the proposed algorithms, we use six...
Deep packet inspection plays a increasingly important role in network security devices and applications, which use more regular expressions to depict patterns. DFA engine is usually used as a classical representation for regular expressions to perform pattern matching, because it only need O(1) time to process one input character. However, DFAs of regular expression sets require large amount of memory,...
Ensemble learning is a commonly used tool for building prediction models from data streams, due to its intrinsic merits of handling large volumes stream data. Despite of its extraordinary successes in stream data mining, existing ensemble models, in stream data environments, mainly fall into the ensemble classifiers category, without realizing that building classifiers requires labor intensive labeling...
Network anomaly detection is a classically difficult research topic in intrusion detection. However, existing research has been solely focused on the detection algorithm. An important issue that has not been well studied so far is the selection of normal training data for network anomaly detection algorithm, which is highly related to the detection performance and computational complexity. Based on...
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