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Shared Nearest Neighbor (SNN) Clustering is a well-established density based clustering algorithm, which can find clusters of different sizes, shapes, and densities. SNN has been widely adopted in numerous applications. As the size of dataset becomes extremely large nowadays, it is inefficient or even impossible for large-scale data to be stored and processed on a single machine. Therefore, the scalability...
With the growing scale of the Internet, the amount of data is increasing rapidly as well. In order to improve the user experience, the recommendation system came into being. It recommends products to the user by analyzing the user's behavior. In the recommendation system, collaborative filtering algorithm is one of the most widely used algorithms. While the traditional collaborative filtering is no...
The core test application time is based on the maximum scan-in/scan-out chains. To design a well balance wrapper scan chains is an important approach to reduce the test application time and test cost. We propose a wrapper scan chains balance algorithm base on twice-assigned algorithm by the chains difference and mean value. By selecting a standard chain with its length L, calculating the mean value...
Clustering is the process of grouping data objects into set of disjoint classes called clusters so that objects within a class are highly similar with one another and dissimilar with the objects in other classes. The k-means algorithm is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local...
Most existing methods perform the projected partition over gene expression data based on the untrue assumption of independence among genes. To address the problem, we propose two novel projected partition algorithms, PPA and PPA+. The basic idea of PPA is to take the order among genes as the criterion of phenotype structure discovery. Specially, in PPA, no any specific data distribution assumption...
This paper presented an improved (1+ε)-randomized approximation algorithm proposed by Ostrovsky. The running time of the improved algorithm is equation, where d,n denote the dimension and the number of the input points respectively, and α(<1) represents the separated coefficient. The successful probability is equation. Compared to the original algorithm, the improved algorithm runs more efficiency.
By analyzing the topological structure of the subway network in Beijing and the people flowing in it, we build the ‘graph model based on the Sub-network Partition Algorithm’ and define the ‘weighted average path length’ to measure the time of the trips in each sub-network. The actual data from the survey proves the correctness of the model.
3D integration is a promising new technology for tightly integrating multiple active silicon layers into a single chip stack. Both the integration of heterogeneous tiers and the partitioning of functional units across tiers leads to significant improvements in functionality, area, performance, and power consumption. Managing the complexity of 3D design is a significant challenge that will require...
The broadcast problem including the plan design is considered. The data are inserted and numbered into customized size relations at a predefined order. The server ability to create a full, regular Broadcast Plan (RBP) with single and multiple channels, after some data transformations, is examined. The Basic Regular Algorithm (BRA) prepares an RBP and enables users to catch their items avoiding wasting...
In this paper, a new Possibilistic C-Spherical Shell clustering (PCSS) algorithm based on conformal geometric algebra is proposed. The probability and simplicity of using the conformal geometric algebra to analyse spherical shell clustering algorithm is discussed firstly. By the conformal geometric algebra theory, patterns and prototypes in spherical shell clustering can be represented as vectors,...
Join queries over wireless sensor data streams need to be processed immediately to keep up with the input streams. Many existing algorithms do not solve the problem in context of both limited CPU and memory resources. In this paper, we propose two CML statistic model based approximate sliding window multi-joins algorithms for the system that both CPU and memory is limited, and a maximum subset of...
A Coarse Grain Reconfigurable Architecture (CGRA) tailored for accelerating bio-informatics algorithms is proposed. The key innovation is a light weight bio-informatics processor that can be reconfigured to perform different Add Compare and Select operations of the popular sequencing algorithms. A programmable and scalable architectural platform instantiates an array of such processing elements and...
Large enterprises have been relying on parallel database management systems (PDBMS) to process their ever-increasing data volume and complex queries. Business intelligence tools used by enterprises frequently generate a large number of outer joins and require high performance from the underlying database systems. A common type of outer joins in business applications is the small-large table outer...
K-means clustering algorithm and one of its enhancements are studied in this paper. Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure. A popular technique for clustering is based on...
Clustering approaches are going to efficiently define the activated regions of the brain in fMRI studies. However, choosing appropriate clustering algorithms and defining optimal number of clusters are still key problems of these methods. In this paper, we apply an improved version of Growing Neural Gas algorithm, which automatically operates on the optimal number of clusters. The decision criterion...
This paper presents an approach which extends a particle swarm optimizer for variable weighting (PSOVW) to handle the problem of text clustering, called text clustering via particle swarm optimization (TCPSO). PSOVW has been exploited for evolving optimal feature weights for clusters and has demonstrated to improve the clustering quality of high-dimensional data. However, when applying it for text...
Clustering algorithms are the core technique of data mining, machine learning, pattern matching, bioinformatics and a number of other fields. This paper proposes a new clustering method based on attribute partitioning and a novel data visualization method. In a nutshell, the idea for our method is based on two steps: 1) cluster data set using primary and secondary attributes of data; 2) map color...
In order to solve the pixel divergence problem brought by print and scan process, propose the algorithm that applying stretching contrast grade to adjust image pixel divergence. The parameter setting of this adjusting method uses peak signal noise ratio as standard of measurement, even though using different printer and scanner, we can also get good adjusting image. Simulation results show that the...
In this paper we introduce a model of Hierarchical Memory with Block Transfer (BT for short). It is like a random access machine, except that access to location x takes time f(x), and a block of consecutive locations can be copied from memory to memory, taking one unit of time per element after the initial access time. We first study the model with f(x) = xα for 0 ≪ α ≪ 1. A tight bound of θ(n log...
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