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Outlier detection is an important data mining task with many contemporary applications. Clustering based methods for outlier detection try to identify the data objects that deviate from the normal data. However, the uncertainty regarding the cluster membership of an outlier object has to be handled appropriately during the clustering process. Additionally, carrying out the clustering process on data...
The rapid growth in the field of data mining has lead to the development of various methods for outlier detection. Though detection of outliers has been well explored in the context of numerical data, dealing with categorical data is still evolving. In this paper, we propose a two-phase algorithm for detecting outliers in categorical data based on a novel definition of outliers. In the first phase,...
Graph mining has been a widely studied domain over the years. Graph representation of real world problems has enabled the development of simple solutions bringing in better clarity. Graph mining has various sub domains among which graph matching is a prominent one having a number of algorithms. With the rise of new applications involving large sets of networked data, the performance of these algorithms...
The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community thanks to its promising results across a variety of applications. However, training AdaBoost on large datasets is a major problem, especially when the...
Deployment of VoIP service brings in security challenges that necessitate additional protection measures for corporate networks. Logging of VoIP sessions between a corporate LAN and the Internet becomes an essential requirement, to prevent misuse, to enforce security policies and to respond to security incidents. Identifying valid VoIP sessions and call tracking from the signaling to media session...
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