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In this paper, we propose a new synchronization-inspired co-clustering algorithm by dynamic simulation, called CoSync, which aims to discover biologically relevant subgroups embedding in a given gene expression data matrix. The basic idea is to view a gene expression data matrix as a dynamical system, and the weighted two-sided interactions are imposed on each element of the matrix from both aspects...
Dynamic networks, especially those representing social networks, undergo constant evolution of their community structure over time. Nodes can migrate between different communities, communities can split into multiple new communities, communities can merge together, etc. In order to represent dynamic networks with evolving communities it is essential to use a dynamic model rather than a static one...
Data stream clustering is an active area of research in big data. It refers to clustering constantly arriving new data records and updating existing cluster patterns and outliers in light of the newly arriving data. Density-based algorithms for solving this problem have the promise for finding arbitrary shape clusters and detecting anomalies without prior knowledge of the number of clusters. In this...
In this paper we consider the data caching problem in next generation data services in the cloud, which is characterized by using monetary cost and access trajectory information to control cache replacements, instead of exploiting capacityoriented strategies as in traditional research. In particular, given a stream of requests to a shared data item with respect to a homogeneous cost model, we first...
K-means algorithm is a classical algorithm and has been widely used in many applications. However, the traditional K-means algorithm is easily influenced by outliers and it usually obtains an unstable clustering result and poor clustering accuracy. In this paper, aiming at K-means algorithm resistant to outliers, we proposed a Capped Robust K-means Algorithm (CRK-means) by adding a capped norm and...
Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability...
Lack of relevant data is a major challenge for Bayesian network (BN) parameters learning. For the issue, this paper proposes a constrained parameter evolutionary learning algorithm (CPEL) which is based on the qualitative knowledge and evolutionary strategy. In detail, firstly qualitative knowledge is employed into BN parameters learning process to reduce the parameter search space where two types...
Cross domain data such as numerical or categorical types are ubiquitous in practical network. Network anomaly detection based on cluster analysis exist some difficulties, for example, the initial center of cluster analysis is sensitive and easy to fall into the local optimal solution. Cross domain data involved great information, but can't be effectively used, which will influence the performance...
For the upcoming IoT (Internet of things) era, plethora of data from a variety of sensors needs to be processed on a real time basis for improving system responsiveness. Due to the increasing modality of sensors, data streaming analytics to deal with high dimensional data becomes a critical ability. In addition, concept drift also needs to be addressed since an IoT-enabled environment is dynamic in...
This article designs a dual-loop energy-saving control scheme for the refrigeration system. To deal with the actual refrigeration system with characteristics of large time-delay, a model-free adaptive control method with input rate constraint of time delay is designed to reduce the impact of large time delay on the whole control process. Simulation study of typical system with large time delay is...
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data. It leverages a time constraint to capture the evolving properties of tensor data. Nowadays the exploding dataset demands a large scale PTTF analysis, and a parallel solution is critical to accommodate the trend. Whereas, the parallelization of PTTF still remains unexplored. In this paper,...
Existing parallel SPARQL query optimizers assume hash-based data partitioning and adopt plan enumeration algorithms with unnecessarily high complexity. Therefore, they cannot easily accommodate other partitioning methods and only consider an unnecessarily limited plan space. To address these problems, we first define a generic RDF data partitioning model to capture the common structure of various...
Nowadays the share of cloud computing technology for hosting applications and services in virtual data centers is constantly growing. This is due to the economic efficiency of IT infrastructure operation. However, avalanche growth of cloud applications and services leads to a number of problems in the existing traditional architecture of data processing centers (DPC). One of such problems is the problem...
Subgraph isomorphism is a fundamental graph problem with many applications. Due to its NP-Hard nature, subgraph isomorphism in large dynamic graphs is considered as a challenging problem. In this paper, we present a distributed graph pruning algorithm (D-IDS) for dynamic graphs to enable efficient subgraph isomorphism. D-IDS continuously maintains the maximum dual simulation match in a dynamic graph...
Expectation-Maximization (EM) is typically used to compute maximum likelihood estimates given incomplete samples and estimated the parameters. We proposed a new algorithm for generating an extension Dynamic Topic Model (exDTM)-in a time-based manner and based on the distribution of documents topics on Spark. The proposed algorithm can be applied in clustering documents from data streams for threat...
Direct reconstruction of parametric images from raw dynamic PET data has the potential of producing lower noise images than obtained using intermediate frame-based reconstructed images, due to the accurately characterized statistical properties of raw PET data. The goal of this study was to extend a previous direct parametric reconstruction algorithm (PMOLAR). PMOLAR uses the Expectation Maximization...
Big data confront many technical challenges that also confront by both academic research communities and commercial IT deployment. Data streams with the curse of dimensionality are founded to be the root sources of Big Data. The commonly used procedure for data sourced from data streams is continuously making batch based model and inducing algorithms which is infeasible for real-time data mining....
In this paper, we present a big-data self organizing network (Bi-SON) framework aiming to optimize energy efficiency of ultra-dense small cells. Although small cell can enhance the capacity of cellular mobile networks, ultra-dense small cells suffer from severe interference and poor energy efficiency. The self organizing network (SON) can automatically manage and optimize the system performance. However,...
Triadic analysis is a convenient way to assess structure and stability of a graph. This paper verifies stability of one triad type that is commonly perceived as transient and unstable, on Instagram subgraph, reconstructed by a specific crawling algorithm. Dynamics of that triad transition has been examined, wrt. degrees of that triad nodes and other basic structural properties of the triad neighborhood...
Existing intelligent theoretical line losses calculation methods that prevalent on worse line calculation error, are all based on single learning algorithm. In order to overcome this defect, a novel intelligent calculation method based on boosting algorithm is proposed. In this calculation method, the theoretical line losses calculation is abstracted into function fitting problem, in addition, the...
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