Clustering interval data through kernel-induced feature space

Advances in mass spectrometry-based proteomics have yielded a substantial mapping of the tyrosine phosphoproteome and thus provided an important step toward a systematic analysis of intracellular signaling networks in higher eukaryotes. In this study we decomposed an uncharacterized proteomics data set of 481 unique phosphotyrosine (Tyr(P)) peptides by sequence similarity to known ligands of the Src homology 2 (SH2) and the phosphotyrosine binding (PTB) domains. From 20 clusters we extracted 16 known and four new interaction motifs. Using quantitative mass spectrometry we pulled down Tyr(P)-specific binding partners for peptides corresponding to the extracted motifs. We confirmed numerous previously known interaction motifs and found 15 new interactions mediated by phosphosites not previously known to bind SH2 or PTB. Remarkably, a novel hydrophobic N-terminal motif ((L/V/I)(L/V/I)pY) was identified and validated as a binding motif for the SH2 domain-containing inositol phosphatase SHIP2. Our decomposition of the in vivo Tyr(P) proteome furthermore suggests that two-thirds of the Tyr(P) sites mediate interaction, whereas the remaining third govern processes such as enzyme activation and nucleic acid binding.

Data sets resulting from physical simulations typically contain a multitude of physical variables. So, visualization methods should take into account the entire multifield volume data rather than concentrate on one variable. We have developed a visualization approach based on surface extraction from multifield volume data. The extracted surfaces segment the data with respect to an underlying multivariate function. Decisions on segmentation properties are based on the analysis of a multidimensional feature space. We perform feature space exploration using automated multidimensional hierarchical clustering. The hierarchical clusters appear as a cluster tree in a 2D radial layout. In this layout, the user can select clusters of interest. A selected cluster in feature space corresponds to a segmenting surface in object space. On the basis of the segmentation property induced by the cluster membership, we extract surfaces from the volume data.

Intuitionistic Fuzzy C-means (IFCM) is a robust clustering method which is based upon intuitionistic fuzzy set theory. It uses Euclidean distance as a distance metric, hence can only cluster hyper spherically distributed data-sets in data space or in feature space. FCM and KFCM with a new distance measure (FCM-σ and KFCM-σ) can detect non-hyperspherical clusters in data space and feature space but they are sensitive to noise and produce inefficient results in the presence of noise. This paper present a robust Intuitionistic Fuzzy c-means(IFCM-σ) and a robust kernel Intutitionistic Fuzzy C-Means(KIFCM-σ) with a new distance metric that incorporates the distance variation in a cluster to regularize the distance between data point and the cluster centroid. Propose algorithms are the hybridization of IFCM, kernel function, and new distance metric in the data space and in the feature space which avoid various problems of IFCM and FCM-σ. Experiments are done using two-dimensional synthetic data-sets and noisy digital images, and results are compared with IFCM, KIFCM, FCM-σ and KFCM-σ. The results show that our proposed algorithms, especially KIFCM-σ are more effective.

Evaluation of the performance of clustering algorithms in kernel-induced feature space

Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel K-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering any type of structured data, provided a kernel which can handle that kind of data is used. We present heuristic methods to find the initial values of the parameters used in our proposed algorithm. To show the effectiveness of this method, this method is applied to two different online handwritten character data sets which are multivariate time series data of varying length, as a real world application. The performance of the proposed method is compared with the spectral clustering and kernel k-means clustering methods. Besides handling outliers, the proposed method performs as well as the spectral clustering method and outperforms the kernel k-means clustering method.

This artificial visual system (AVS) is a computational framework for computer vision based on spatial filtering and statistical pattern recognition. Computer vision tasks are often poorly defined; the AVS clarifies the kinds of visual tasks that can be defined and what constitutes a well-defined task. `Segmentation'' is not a well-defined task. Edge detection is revealed to be an absurd task. A filter set composed of multiscale Gaussians alone captures the structure of Koenderink''s generic neighborhood operators when a pattern is constructed from the responses at a pixel and neighboring locations, where the distance to the selected neighbors increases with larger scale. Prior studies of the feature space formed by multiscale Gaussians reveal surprising power in the multiscale Gaussians alone. New studies support this observation. Contrary to common belief, we show how nonlocal, spatial, geometric structure can be captured using statistical pattern recognition operations in the AVS framework. A procedure is defined for deriving a single composite filter providing optimal separation of two clusters in feature space.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An unsupervised texture segmentation system using texture features obtained from a combination of spatial filters and nonlinear operators is described. Local texture features are evaluated in parallel by a succession of four basic operations: (1) a convolution for local structure detection (local linear transform); (2) a first nonlinearity of the form f(x)= mod x mod /sup alpha /; (3) an iterative smoothing operator; and (4) a second nonlinearity g(x). The Karhunen-Loeve transform is used to reduce the dimensionality of the resulting feature vector, and segmentation is achieved by thresholding or clustering in feature space. The combination of nonlinearities f(x)= mod x mod /sup alpha / (in particular, alpha =2) and g(x)=log x maximizes texture discrimination, and results in a description with variances approximately constant for all feature components and texture regions. This latter property improves the performance of both feature reduction and clustering algorithms significantly. >

Cluster analysis is an important and frequently-used data mining tool with a wide variety of research applications. There are numerous methods by which clusters in a dataset can be identified, with the majority of tools used for identifying statistical clusters in feature space. There also exists a specific subset of clustering approaches appropriate for the identification of spatial and spatiotemporal clusters with geographic data. The theory of these methods is presented here, as are common pitfalls and parameterization methods. This chapter concludes with a detailed discussion on how several different common spatial and spatiotemporal cluster analyses can be applied to different datasets.

Clustering has been used in literature to enhance classification accuracy. But most partitional clustering methods need the number of clusters as input and also they are sensitive to initialization. Although hierarchical clustering methods may be more effective in finding clustering structure of the dataset than partitional methods but hierarchical clustering methods give tree structure known as dendrogram which is a sequence of clustering solutions. Hence hierarchical clustering algorithms are not generally applied in the preprocessing step to classification methods. This problem can be solved by cutting the dendrogram to get single clustering solution. In this paper we propose a framework for classification which uses Optimal Clustering Genetic Algorithm (OCGA) to obtain optimal level of cutting the dendrogram. A single clustering solution is obtained by cutting the dendrogram at optimal level. The clusters obtained are used to enhance classification accuracy of the classification methods. The proposed classification methods have been applied for the diagnosis of diabetes disease.

We propose a soft-constrained clustering method for group-based labeling of medical images. Since the idea of group-based labeling is to attach the label to a group of samples at once, we need to have groups (i.e., clusters) with high purity. The proposed method is formulated to achieve high purity even for difficult clustering tasks such as medical image clustering, where image samples of the same class are often very distant in their feature space. In fact, those images degrade the performance of conventional constrained clustering methods. Experiments with an endoscopy image dataset demonstrated that our method outperformed various state-of-the-art methods.

Gaussian Mixture Density Modelling and Decomposition (GMDD) is a hierarchical clustering method based on robust statistical theory. Firstly, GMDD is assumed with a mixture group of Gaussian distribution in feature space, then by optimization algorithm the feature which mostly accord with the assumed distribution is hierarchically extracted from space until all of the features in the space are decomposed to a group of featuring pattern. Compared with conventional statistical clustering methods, GMDD's main outstanding superorities are:(1)Initial number of features does not need to be specified a priori; (2)The proportion of noisy data in the mixture can be large; (3)The parameters estimation of each feature is virtually initial independent; and (4)The variability in the shape and size of the feature densities in the mixture is taken into account. The article presented the model named the GMDD based remote sensing image feature estimation model (GIFEM) , and the model of GA space searching optimization is also presented out.

Spatial clustering has been identified as an important technique in data mining owing to its various applications. In the conventional spatial clustering methods, data points are clustered mainly according to their geographic attributes. In real applications, however, the obtained data points consist of not only geographic attributes but also non-geographic ones. In general, geographic attributes indicate the data locations and non-geographic attributes show the characteristics of data points. It is thus infeasible, by using conventional spatial clustering methods, to partition the geographic space such that similar data points are grouped together. In this paper, we propose an effective and efficient algorithm, named incremental clustering toward the Bound INformation of Geography and Optimization spaces, abbreviated as BINGO, to solve the problem. The proposed BINGO algorithm combines the information in both geographic and non-geographic attributes by constructing a summary structure and possesses incremental clustering capability by appropriately adjusting this structure. Furthermore, most parameters in algorithm BINGO are determined automatically so that it is easy to be applied to applications without resorting to extra knowledge. Experiments on synthetic are performed to validate the effectiveness and the efficiency of algorithm BINGO.

Detecting biological associations between genes based on the theory of phase synchronization

BackgroundThe combination of systematic evolution of ligands by exponential enrichment (SELEX) and deep sequencing is termed high-throughput (HT)-SELEX, which enables searching aptamer candidates from a massive amount of oligonucleotide sequences. A clustering method is an important procedure to identify sequence groups including aptamer candidates for evaluation with experimental analysis. In general, aptamer includes a specific target binding region, which is necessary for binding to the target molecules. The length of the target binding region varies depending on the target molecules and/or binding styles. Currently available clustering methods for HT-SELEX only estimate clusters based on the similarity of full-length sequences or limited length of motifs as target binding regions. Hence, a clustering method considering the target binding region with different lengths is required. Moreover, to handle such huge data and to save sequencing cost, a clustering method with fast calculation from a single round of HT-SELEX data, not multiple rounds, is also preferred.ResultsWe developed fast string-based clustering (FSBC) for HT-SELEX data. FSBC was designed to estimate clusters by searching various lengths of over-represented strings as target binding regions. FSBC was also designed for fast calculation with search space reduction from a single round, typically the final round, of HT-SELEX data considering imbalanced nucleobases of the aptamer selection process. The calculation time and clustering accuracy of FSBC were compared with those of four conventional clustering methods, FASTAptamer, AptaCluster, APTANI, and AptaTRACE, using HT-SELEX data (>15 million oligonucleotide sequences). FSBC, AptaCluster, and AptaTRACE could complete the clustering for all sequence data, and FSBC and AptaTRACE performed higher clustering accuracy. FSBC showed the highest clustering accuracy and had the second fastest calculation speed among all methods compared.ConclusionFSBC is applicable to a large HT-SELEX dataset, which can facilitate the accurate identification of groups including aptamer candidates.Availability of data and materialsFSBC is available at http://www.aoki.ecei.tohoku.ac.jp/fsbc/.

With the increase of marine industry in the Beibu Gulf, data clustering has become an important task of intelligent ocean. Partition clustering methods are suitable for marine data. However, traditional K-means algorithm is not suitable for large scale data. Focusing on the characteristics of oceanic big data, we propose a clustering method based on incremental K-means (IKM) algorithm. First, a vector model is adopted to represent data sets, and the calculation model for mean values and centres is used to initialise arbitrary numbers of data points. Second, the input data vectors are iteratively calculated in an incremental vector form. Finally, by applying incremental vector and distance model, the large-scale data are clustered according to convergence condition. Experiments show that the algorithm can increase the clustering efficiency, reduce time and space complexity, and lower the missing data rate.