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101.
Document clustering using synthetic cluster prototypes 总被引:3,自引:0,他引:3
Argyris Kalogeratos Author VitaeAristidis LikasAuthor Vitae 《Data & Knowledge Engineering》2011,70(3):284-306
The use of centroids as prototypes for clustering text documents with the k-means family of methods is not always the best choice for representing text clusters due to the high dimensionality, sparsity, and low quality of text data. Especially for the cases where we seek clusters with small number of objects, the use of centroids may lead to poor solutions near the bad initial conditions. To overcome this problem, we propose the idea of synthetic cluster prototype that is computed by first selecting a subset of cluster objects (instances), then computing the representative of these objects and finally selecting important features. In this spirit, we introduce the MedoidKNN synthetic prototype that favors the representation of the dominant class in a cluster. These synthetic cluster prototypes are incorporated into the generic spherical k-means procedure leading to a robust clustering method called k-synthetic prototypes (k-sp). Comparative experimental evaluation demonstrates the robustness of the approach especially for small datasets and clusters overlapping in many dimensions and its superior performance against traditional and subspace clustering methods. 相似文献
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一、引言自然界以及我们社会生活中的各种事物都在运动、变化和发展着,将它们按时间顺序记录下来,我们就可以得到各种各样的“时间序列”数据。对时间序列进行分析,可以揭示事物运动、变化和发展的内在规律,对于人们正确认识事物并据此作出科学的决策具有重要的现实意义。 相似文献
106.
本文提出了大型管理信息系统网络环境逻辑设计的方法和过程。通过引用信息系统需求分析的有关数据对数据和应用进行模糊聚类分析,研究数据和应用之间的内在关系,划分出各个子系统,计算出各子系统之间的数据流量,依此为依据,进行信息系统网络逻辑设计。 相似文献
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108.
Predictive modelling of online dynamic user-interaction recordings and community identification from such data becomes more
and more important with the widespread use of online communication technologies. Despite of the time-dependent nature of the
problem, existing approaches of community identification are based on static or fully observed network connections. Here we
present a new, dynamic generative model for the inference of communities from a sequence of temporal events produced through
online computer- mediated interactions. The distinctive feature of our approach is that it tries to model the process in a
more realistic manner, including an account for possible random temporal delays between the intended connections. The inference
of these delays from the data then forms an integral part of our state-clustering methodology, so that the most likely communities
are found on the basis of the likely intended connections rather than just the observed ones. We derive a maximum likelihood
estimation algorithm for the identification of our model, which turns out to be computationally efficient for the analysis
of historical data and it scales linearly with the number of non-zero observed (L + 1)-grams, where L is the Markov memory length. In addition, we also derive an incremental version of the algorithm, which could be used for
real-time analysis. Results obtained on both synthetic and real-world data sets demonstrate the approach is flexible and able
to reveal novel and insightful structural aspects of online interactions. In particular, the analysis of a full day worth
synchronous Internet relay chat participation sequence, reveals the formation of an extremely clear community structure. 相似文献
109.
Many mal-practices in stock market trading—e.g., circular trading and price manipulation—use the modus operandi of collusion. Informally, a set of traders is a candidate collusion set when they have “heavy trading” among themselves, as compared to their trading with others. We formalize the problem of detection of collusion sets, if any, in the given trading database. We show that naïve approaches are inefficient for real-life situations. We adapt and apply two well-known graph clustering algorithms for this problem. We also propose a new graph clustering algorithm, specifically tailored for detecting collusion sets. A novel feature of our approach is the use of Dempster–Schafer theory of evidence to combine the candidate collusion sets detected by individual algorithms. Treating individual experiments as evidence, this approach allows us to quantify the confidence (or belief) in the candidate collusion sets. We present detailed simulation experiments to demonstrate effectiveness of the proposed algorithms. 相似文献
110.
Data co-clustering refers to the problem of simultaneous clustering of two data types. Typically, the data is stored in a
contingency or co-occurrence matrix C where rows and columns of the matrix represent the data types to be co-clustered. An entry C
ij
of the matrix signifies the relation between the data type represented by row i and column j. Co-clustering is the problem of deriving sub-matrices from the larger data matrix by simultaneously clustering rows and
columns of the data matrix. In this paper, we present a novel graph theoretic approach to data co-clustering. The two data
types are modeled as the two sets of vertices of a weighted bipartite graph. We then propose Isoperimetric Co-clustering Algorithm
(ICA)—a new method for partitioning the bipartite graph. ICA requires a simple solution to a sparse system of linear equations
instead of the eigenvalue or SVD problem in the popular spectral co-clustering approach. Our theoretical analysis and extensive
experiments performed on publicly available datasets demonstrate the advantages of ICA over other approaches in terms of the
quality, efficiency and stability in partitioning the bipartite graph. 相似文献