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RFID technology provides a powerful ability of perceiving the world for human and it produces vast amounts of data. how to store and analyze the mass information has become a new challenge. A novel data structure for management and storage of RFID data is proposed in this paper, which uses a improvement form of the T tree - T list of trees (Dual T tree) and a path encoding technique to build spatio-temporal...
Mass estimation, an alternative to density estimation, has been shown recently to be an effective base modelling mechanism for three data mining tasks of regression, information retrieval and anomaly detection. This paper advances this work in two directions. First, we generalise the previously proposed one-dimensional mass estimation to multidimensional mass estimation, and significantly reduce the...
The k-means clustering algorithm is a widely used scheme to solve the clustering problem which classifies a given set of n data points in m-dimensional space into k clusters, whose centers are obtained by the centroids of the points in the same cluster. The problem with privacy consideration has been studied, when the data is distributed among different parties and the privacy of the distributed data...
Recently, novel identification methods have been proposed based on orthogonal basis nonlinear functions. These methods present strong statistical convergence properties but have not been evaluated from a computational point of view. This paper investigates the computational cost and performance of an orthogonal basis nonlinear system identification method. The computational effort of the model estimation...
This article was created to investigate the triangular irregular network (TIN) problem algorithms with the Delaunay condition. Nowadays TIN problem is used on many fields as a problem to find the way, electronic maps, traffic problems and etc. In HoChiMinh city, TIN problem also is used to apply for civic plan subsystems, traffic subsystems, electric power subsystems, and post subsystems...This article...
We propose a probabilistic, non-intrusive method for quality assessment of speech that takes into consideration the bounded character of the preference scores. The quality ratings are modeled as iid Beta random variables, whose mean and precision are parametrized directly in terms of the signal features. Maximum likelihood estimation is used to learn the model parameters in view of a training database...
This work presents a new approximation for the Kolmogorov complexity of strings based on compression with smallest Context Free Grammars (CFG). If, for a given string, a dictionary containing its relevant patterns may be regarded as a model, a Context-Free Grammar may represent a generative model, with all of its rules (and as a consequence its own size) being meaningful. Thus, we define a new complexity...
In this paper a method for controlled simplification is presented, which is able to create simplified models with specific properties concerning complexity and behavioral deviation automatically. The method requires a finite set of model component classes, of which instances a user-defined model can be created. Two techniques for simplification are used: aggregation, where a large set of components...
Association link network (ALN) is used to establish associated relations among various resources, aiming at extending the hyperlink network World Wide Web to an association-rich network, for effectively supporting Web intelligence activities. Unfortunately, with the increase number of Web resources, the challenge of incremental building of ALN is on how to perform the association weight of the new...
Temporal XML has attracted more and more attention in recent researches. Temporal XML can represent temporal data, track historical information and recover the state of the document as of any given time. This paper is devoted to the technique and the implementation of temporal XML indexing. Firstly, we introduce temporal XML, suffix tree and semi-structured data model (OEM), and a temporal XML indexing...
In a multi-parameter learning problem, besides choosing the architecture of the learner, there is the problem of finding the optimal parameters to get maximum performance. When the number of parameters to be tuned increases, it becomes infeasible to try all the parameter sets, hence we need an automatic mechanism to find the optimum parameter setting using computationally feasible algorithms. In this...
In this paper, an optimal aggregation and counter-aggregation (drill-down) methodology is proposed on multidimensional data cube. The main idea is to aggregate on smaller cuboids after partitioning those depending on the cardinality of the individual dimensions. Based on the operations to make these partitions, a Galois Connection is identified for formal analysis that allow to guarantee the soundness...
Simulation and visualization of emergent crowd in real-time is a computationally intensive task. This intensity mostly comes from the O(n2) complexity of the traversal algorithm, necessary for the proximity queries of all pair of entities in order to compute the relevant mutual interactions. Previous works reduced this complexity by considerably factors, using adequate data structures for spatial...
RVM enables sparse classification and regression functions to be obtained by linearly-weighting a small number of fixed basis functions from a large dictionary of potential candidates.TOA on RVM has O(M3) time and O(M2) space complexity, where M is the training set size. It is thus computationally infeasible on very large data sets. We propose I-CBA based on CBA, I-CBA set iteration initial center...
We show that every finitely satisfiable two-variable first-order formula with two equivalence relations has a model of size at most triply exponential with respect to its length. Thus the finite satisfiability problem for two-variable logic over the class of structures with two equivalence relations is decidable in nondeterministic triply exponential time. We also show that replacing one of the equivalence...
RVM enables sparse classification and regression functions to be obtained by linearly-weighting a small number of fixed basis functions from a large dictionary of potential candidates.TOA on RVM has O(M3) time and O(M2) space complexity, where M is the training set size. It is thus computationally infeasible on very large data sets. We propose CBA . it decomposed large datasets to subdata blocks by...
Credit scoring is a typical binary classification problem. Its significance to financial institutions has brought application of many quantitative methods. Most published research is focused on increasing classification performance by adjusting algorithms, generally without a corresponding analysis of intrinsic dataset difficulties. Prior research shows that these intrinsic difficulties cause all...
Efficient algorithms with time fading model for mining frequent items over data stream are presented. Our algorithm FC2 can detectepsiv-approximate frequent items of a data stream using O(epsiv-1) memory space and the processing time for each data item is O(1). Experimental results on several artificial data sets and real data sets show that our methods have high precision, require less memory and...
We use a scalar function thetas to describe the complexity of data compression systems based on vector quantizers (VQs). This function is associated with the analog hardware implementation of a VQ, as done for example in focal-plane image compression systems. The rate and distortion of a VQ are represented by a Lagrangian cost function J. In this work we propose an affine model for the relationship...
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