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21.
22.
Traditional clustering algorithms are inapplicable to many real-world problems where limited knowledge from domain experts
is available. Incorporating the domain knowledge can guide a clustering algorithm, consequently improving the quality of clustering.
In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for data clustering. In SS-NMF,
users are able to provide supervision for clustering in terms of pairwise constraints on a few data objects specifying whether
they “must” or “cannot” be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the
data similarity matrix to infer the clusters. Theoretically, we show the correctness and convergence of SS-NMF. Moveover,
we show that SS-NMF provides a general framework for semi-supervised clustering. Existing approaches can be considered as
special cases of it. Through extensive experiments conducted on publicly available datasets, we demonstrate the superior performance
of SS-NMF for clustering.
相似文献
Ming DongEmail: |
23.
The ability to reliably merge independent updates of a document is a crucial prerequisite to efficient collaboration in office work. However, merge support for common office document standards like OpenDocument or OfficeOpenXML is still in its infancy. In this paper, we present a consistent versioning model for XML documents in general including merge support. This is achieved by using context-aware fingerprints that identify edit operations and allow for a conflict detection. We show how to extract tracked changes from office documents and map them on our delta model. Experimental results indicate that our fingerprinting technique is efficient and reliable. 相似文献
24.
A new algebraic structure for formal concept analysis 总被引:1,自引:0,他引:1
Formal concept analysis (FCA) originally proposed by Wille [39], is an important theory for data analysis and knowledge discovery. Concept lattice is the core of the mathematical theory of formal concept analysis. To address the requirements of real word applications, concept lattice has been extended to many other forms from the theoretical point of view and possible applications. In this paper, with the aim of deriving the mathematical properties of formal concepts from the point of algebra, we propose a new algebra system for the formal context. Under the frame of the proposed system, some interesting properties of formal concepts are explored, which could be applied to explore concept hierarchy and ontology merging. 相似文献
25.
Improving the spatio-temporal distribution of surface solar radiation data by merging ground and satellite measurements 总被引:2,自引:0,他引:2
Appropriate information on solar resources is very important for a variety of technological areas, such as: agriculture, meteorology, forestry engineering, water resources and in particular in the designing and sizing of solar energy systems. However, the availability of observed solar radiation measurements has proven to be spatially and temporally inadequate for many applications. In this paper we propose to merge the global solar radiation measurements from the Royal Meteorological Institute of Belgium solar measurements network with the operationally derived surface incoming global short-wave radiation products from Meteosat Second Generation satellites imageries to improve the spatio-temporal resolution of the surface global solar radiation data over Belgium. We evaluate several merging methods with various degrees of complexity (from mean field bias correction to geostatistical merging techniques) together with interpolated ground measurements and satellite-derived values only. The performance of the different methods is assessed by leave-one-out cross-validation. 相似文献
26.
27.
Guoxian YuAuthor Vitae Hong PengAuthor VitaeJia WeiAuthor Vitae Qianli MaAuthor Vitae 《Neurocomputing》2011,74(4):598-605
Curse of dimensionality is a bothering problem in high dimensional data analysis. To enhance the performances of classification or clustering on these data, their dimensionalities should be reduced beforehand. Locality Preserving Projections (LPP) is a widely used linear dimensionality reduction method. It seeks a subspace in which the neighborhood graph structure of samples is preserved. However, like most dimensionality reduction methods based on graph embedding, LPP is sensitive to noise and outliers, and its effectiveness depends on choosing suitable parameters for constructing the neighborhood graph. Unfortunately, it is difficult to choose effective parameters for LPP. To address these problems, we propose an Enhanced LPP (ELPP) using a similarity metric based on robust path and a Semi-supervised ELPP (SELPP) with pairwise constraints. In comparison with original LPP, our methods are not only robust to noise and outliers, but also less sensitive to parameters selection. Besides, SELPP makes use of pairwise constraints more efficiently than other comparing methods. Experimental results on real world face databases confirm their effectiveness. 相似文献
28.
Region merging methods consist of improving an initial segmentation by merging some pairs of neighboring regions. In a graph,
merging two regions, separated by a set of vertices, is not straightforward. The perfect fusion graphs defined in J. Cousty
et al. (J. Math. Imaging Vis. 30:(1):87–104, 2008) verify all the basic properties required by region merging algorithms as used in image segmentation. Unfortunately, the
graphs which are the most frequently used in image analysis (namely, those induced by the direct and the indirect adjacency
relations) are not perfect fusion graphs. The perfect fusion grid, introduced in the above mentioned reference, is an adjacency
relation on ℤ
d
which can be used in image analysis, which indeed induces perfect fusion graphs and which is “between” the graphs induced
by the direct and the indirect adjacencies. One of the main results of this paper is that the perfect fusion grid is the only
such graph whatever the dimension d.
相似文献
Gilles BertrandEmail: |
29.
信息瓶颈(Information Bottleneck,IB)方法在处理非平衡数据集时,倾向于将大簇中的数据对象划分到数据规模较小的小簇中,造成了聚类效果不理想的问题。针对该问题,提出了一种面向非平衡数据的多簇信息瓶颈算法(McIB)。McIB算法采用向下抽样方法来降低非平衡数据集的倾斜度,使用先划分再学习后合并的策略来优化IB算法处理非平衡数据的合并抽取过程。整个算法包含3步:首先根据分离标准来确定抽样比例参数;然后对数据进行初步的聚类,生成可信赖的多个簇;最后再利用簇之间的相似性对簇进行合并,组织多个簇代表每个实际的簇来得到最终的聚类结果。实验结果表明:所提算法能够有效地解决IB方法在非平衡数据集上的“均匀效应”问题;与其他聚类算法相比,McIB算法的性能更优。 相似文献
30.
CFSFDP是基于密度的新型聚类算法,可聚类非球形数据集,具有聚类速度快、实现简单等优点。然而该算法在指定全局密度阈值dc时未考虑数据空间分布特性,导致聚类质量下降,且无法对多密度峰值的数据集准确聚类。针对以上缺点,提出基于网格分区的CFSFDP(简称GbCFSFDP)聚类算法。该算法利用网格分区方法将数据集进行分区,并对各分区进行局部聚类,避免使用全局dc,然后进行子类合并,实现对数据密度与类间距分布不均匀及多密度峰值的数据集准确聚类。两个典型数据集的仿真实验表明,GbCFSFDP算法比CFSFDP算法具有更加精确的聚类效果。 相似文献