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As one of the most popular social media platforms, Twitter has become a tool that people widely used to share their contents, their interests and events with friends. Meanwhile, we are facing a big challenge to find the bursty events from the large volume of continuous text streams quickly and accurately due to millions of data produced every day. In this paper, we proposed a BBW (Basic-Burst Weight)...
Discovering social communities or social circles from social networks is interesting and important for many applications like business advertisement, social recommendation and collaborative office. In this paper, by integrating grey relational analysis with the label propagation algorithm and the parallel framework, a new parallel algorithm for detecting overlapping communities is proposed. The similarity...
Feature selection for clustering is a challenging problem due to the absence of class labels. Existing approaches can select a feature subset to maintain clustering performance while reducing dimensionality. However, we are faced with two problems: (1) there could be many sets of features that seem equally good, and (2) these features are sensitive to small data perturbation, or the selection instability...
With rapid advances in technology and connectivity, the capability to capture data from multiple sources has given rise to multiview learning wherein each object has multiple representations and a learned model, whether supervised or unsupervised, needs to integrate these different representations. Multiview learning has shown to yield better predictive and clustering models, it also is able to provide...
With the explosive increase of data volume, the research of data quality and data usability draws extensive attention. In this work, we focus on one aspect of data usability -- incomplete data imputation, and present a novel missing value imputation method using stacked auto-encoder and incremental clustering (SAICI). Specifically, SAICI's functionality rests on four pillars: (i) a distinctive value...
The performance of network intrusion detection systems based on machine learning techniques largely depends on the selected features. However, choosing the optimal subset of features from a given feature set requires extensive computing resources. To tackle this problem we propose an optimal feature selection algorithm based on a local search algorithm. In order to evaluate the performance of our...
NeuroinformaticsNatural Language Processing (NeuroNLP) relies on clustering and classification for information categorization of biologically relevant extraction targets and for interconnections to knowledge-related patterns in event and text mined datasets. The accuracy of machine learning algorithms depended on quality of text-mined data while efficacy relied on the context of the choice of techniques...
Support Vector Machines (SVMs) were primarily designed for 2-class classification. But they have been extended for N-class classification also based on the requirement of multiclasses in the practical applications. Although N-class classification using SVM has considerable research attention, getting minimum number of classifiers at the time of training and testing is still a continuing research....
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
We proposed a scalable outlier detection method to identify outliers in large datasets with a goal to create unsupervised intrusion detection. In our work, the strength of Kolmogorov-Smirnov test and K-means clustering algorithm, both with linear time complexity, are combined to create fast outlier detection. While still maintaining high detection rate and low false alarm rate, our method can easily...
A new method for fuzzy categorization of cursive handwritten text has been addressed in the present work. This is based on input text clustering and subsequent learning of weighted attributes in each subject cluster. The system first employs a new algorithm to detect the letter boundary in each cursive word in the textual sentences. A Modified Optimal Clustering Algorithm (MOCA) and Back Propagation...
A way of combining SVM(Support Vector Machine) with Supervised Subset Density Clustering is proposed in this paper. How to minimize the training set of SVM by means of clustering is researched. Original center positions are of great importance to clustering accuracy. However the traditional clustering center choosing algorithm doesn't work properly when the same kind of samples aren't closely-spaced...
Location estimation is essential to the success of location based services. Since GPS does not work well in indoor and the urban areas, several indoor localization systems have been proposed in the literature. Among these, the fingerprinting-based localization systems involving two phases: training phase and positioning phase, are used mostly. In the training phase, a radio map is constructed by collecting...
This paper presents algorithm and digital hardware design, inspired by biological spiking neural networks, to perform unsupervised, online spike-clustering with high accuracy and low-power consumption in the context of deep-brain sensing and stimulation systems. The proposed hardware contains 1220 digital neurons and 4.86k latch-based synapses, and achieves the average sorting accuracy of 91% whereas...
In this study, we apply an anomaly-based approach to analyze traffic flows transferred over a network to detect the flows related to different types of attacks. Based on the information extracted from network flows a model of normal user behavior is discovered with the help of several clustering techniques. This model is then used to detect anomalies within recent time intervals. Since this approach...
Approximate spectral clustering (ASC), a recently popular approach for unsupervised land cover identification, applies spectral clustering on a reduced set of data representatives (found by sampling or quantization). ASC enables extraction of clusters with different characteristics by utilizing various information types (such as distance, local density distribution and data topology) for accurate...
Air pollutants are really a hazardous problem in Bangladesh. This paper works on the relationship between the pollutants and the admittance of patients in the medical facilities and analyzes the reason behind the increase of the disease rate in the hospitals. The research collected medical data from the medical center named National Institute of Disease of the Chest and Hospital (NIDCH) that is located...
Live streaming services have been developed prosperously in recent years. With live streaming services, broadcasters broadcast their videos to attract large numbers of viewers to watch. Some live streaming services also provide a platform for viewers gathering together to watch channels and interact with others. Over hundreds of videos broadcast every day, recommending channels are necessary to help...
QoS prediction for Web services is a hot research problem in the field of services computing. As one of the most important methods for QoS prediction, Collaborative Filtering (CF) makes prediction based on the historical QoS data contributed by similar users and services. The key issue in this process is to detect the unreliable data offered by untrustworthy users, which has attracted limited attentions...
The openness of the Web exposes opportunities for criminals to upload malicious content. In fact, despite extensive research, email based spam filtering techniques are unable to protect other web services. Therefore, a counter measure must be taken that generalizes across web services to protect the user from phishing host URLs. This paper describes an approach that classifies URLs automatically based...
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