Affinity Propagation Clustering Method Research Articles

Evaluating the multi-scale patterns of jobs-residence balance and commuting time–cost using cellular signaling data: a case study in Shanghai

Assessing jobs-housing balance (JHB) and commuting efficiency is crucial to urban and transport planning. However, the scale dependency problem, meaning that metrics may be biased as they rely on an arbitrary search radius or pre-defined jurisdictional division, remains unresolved. This paper proposes to apply clustering method to multi-scale indicators for evaluating aggregate patterns of jobs-housing relationship and commuting time–cost. We based our analysis on individual commuters extracted from cell phone data, a reliable substitute for travel surveys. After the commuter origin-destinations are carefully inferred, we draw two types of curves to represent the multi-scale characteristics of jobs-residence relationship and commuting efficiency. One is the multi-scale index curve (MSIC) of jobs-residence ratio that measured within multiple scales. Another is the probability density curve (PDC) of commuting time–cost. Then we applied the affinity propagation clustering method to the curves and detected six patterns of MSIC and ten of PDC. By comparing our result with the conventional methods we showed that the latter’s classification accuracy never exceeds 60% when an arbitrary radius or metric is used. Moreover, we correlated the jobs-residence ratio with the average commuting time–cost and confirmed a positive effect of JHB, which, however, also varies dramatically with different search radius. Additionally, we conducted an overlay analysis of the MSIC and PDC patterns and obtained eight joint-patterns as well as spatial divisions which contributes effectively to our understanding of the commuting issues in Shanghai.

DC cable feature extraction based on the PD image in the non-subsampled contourlet transform domain

A new method to extract the features of direct current (DC) cross linked polyethylene (XLPE) cable partial discharge (PD) image based on non-subsampled contourlet transform (NSCT) is proposed in this paper. Four types of PD images are obtained from the artificially designed cable insulation defects. The features include Shannon entropy, Renyi entropy, Euclidean distance, and Mahalanobis distance of the optimized subband coefficients by NSCT. The optimized subband coefficients are selected by the immune algorithm optimized affinity propagation clustering method. The back propagating neural network, k-nearest neighbor, support vector machine and decision tree algorithms are used to classify the extracted features. The experimental results show that the proposed method can be applied to the feature extraction of PD signals in DC XLPE cables. Compared with the wavelet method, the recognition rate of the method is improved by more than 16.80% under the same classifier. It is found that the type of defect within insulation can be classified efficiently with the features extracted by the proposed method with a reasonable degree of accuracy.

A Bibliometric Review of Natural Language Processing Empowered Mobile Computing

Natural Language Processing (NLP) empowered mobile computing is the use of NLP techniques in the context of mobile environment. Research in this field has drawn much attention given the continually increasing number of publications in the last five years. This study presents the status and development trend of the research field through an objective, systematic, and comprehensive review of relevant publications available from Web of Science. Analysis techniques including a descriptive statistics method, a geographic visualization method, a social network analysis method, a latent dirichlet allocation method, and an affinity propagation clustering method are used. We quantitatively analyze the publications in terms of statistical characteristics, geographical distribution, cooperation relationship, and topic discovery and distribution. This systematic analysis of the field illustrates the publications evolution over time and identifies current research interests and potential directions for future research. Our work can potentially assist researchers in keeping abreast of the research status. It can also help monitoring new scientific and technological development in the research field.

An Affinity Propagation Clustering Method Using Hybrid Kernel Function With LLE

Cluster analysis is important in data mining and clustering algorithms and has gained much attention during the last decade. However, it is a challenge to extract significant features from high-dimensional data and to rapidly provide satisfactory clustering results. This paper presents a new affinity propagation (AP) clustering method based on a hybrid kernel function with locally linear embedding, called LLE-HKAP, for the classification of gene expression datasets and standard UCI datasets. First, the locally linear embedding algorithm is used to reduce the dimension of the original dataset. Then, a novel AP clustering method based on a similarity measure with the hybrid kernel function is proposed. In this method, a new global kernel is defined that has high generalization ability. Meanwhile, a hybrid kernel function that linearly combines the proposed global kernel and the Gaussian kernel is defined to further enhance the learning ability of the global kernel. Moreover, the novel hybrid kernel is introduced to define a similarity measure and construct a similarity matrix of the AP clustering. Finally, the improved AP clustering algorithm is implemented on eight public gene expression datasets and eight standard UCI datasets for comparison with other related algorithms. The experimental results validate that our proposed clustering algorithm is efficient in terms of clustering accuracy and outperforms the currently available approaches with which it is compared.

Wireless fingerprinting indoor positioning using affinity propagation clustering methods

According to rapid extension of wireless sensor network localization, indoor localization using fingerprint has turned out to be more considerable lately. It contains of a database called Receive Strength Signal Indicator vectors, which is a primitive amount in wireless sensor network fingerprinting positioning. The equivalence of a few strategies is brought up from the literary works, and some new variants are presented in this study. A combination of a clustering strategy named affinity propagation and statistical and probabilistic positioning procedures is considered in this review and at the same time, the impact of some components in our methodology onto positioning precision will be investigated. Affinity propagation clustering method set up a common baseline for assessing the relative accuracy of various indoor location methods effectively. Eventually two coarse localization methods as Mahalanobis norm method and similarity to exemplar receive strength signal vector are compared based on positioning accuracy and performance. Experimental outcomes prove that the intended algorithm will advance the accuracy and localization error compared with the method without clustering.

MultiBoost with ENN-based ensemble fault diagnosis method and its application in complicated chemical process

Fault diagnosis plays an important role in complicated industrial process. It is a challenging task to detect, identify and locate faults quickly and accurately for large-scale process system. To solve the problem, a novel MultiBoost-based integrated ENN (extension neural network) fault diagnosis method is proposed. Fault data of complicated chemical process have some difficult-to-handle characteristics, such as high-dimension, non-linear and non-Gaussian distribution, so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features. Then, the affinity propagation (AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time. Afterwards, an integrated ENN classifier based on MultiBoost strategy is constructed to identify fault types. The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters. Finally, a real industrial system—Tennessee Eastman (TE) process is employed to evaluate the performance of the proposed method. And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.

An extended affinity propagation clustering method based on different data density types.

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

Multiple kernel-based multi-instance learning algorithm for image classification

In this paper, a novel multi-instance learning (MIL) algorithm based on multiple-kernels (MK) framework has been proposed for image classification. This newly developed algorithm defines each image as a bag, and the low-level visual features extracted from its segmented regions as instances. This algorithm is started from constructing a “word-space” from instances based on a collection of “visual-words” generated by affinity propagation (AP) clustering method. After calculating the distance between a “visual-word” and the bag (image), a nonlinear mapping mechanism is introduced for registering each bag as a coordinate point in the “word-space”. In this case, the MIL problem is transformed into a standard supervised learning problem, which allows multiple-kernels support vector machine (MKSVM) classifiers to be trained for the image categorization. Compared with many popular MIL algorithms, the proposed method, named as MKSVM-MIL, shows its satisfactorily experimental results on the COREL dataset, which highlights the robustness and effectiveness for image classification applications.

A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering

Clustering analysis has been widely used to detect the functional connectivity from functional magnetic resonance imaging (fMRI) data. However, it has some limitations such as enormous computer memory requirement, and difficulty in estimating the number of clusters. In this study, in order to effectually resolve the deficiencies mentioned above, we have proposed a novel approach (SAAPC) for fMRI data analysis, which combines sparsity, an effective assumption for analyzing fMRI signal, with affinity propagation clustering (APC).The SAAPC method is composed of three parts: to obtain the sparse approximation coefficients set through wavelet packet decomposition and sparsity measuring and selection, which contributes a lot in the brain functional connectivity detection accuracy; to implement a split APC algorithm, which is put forward in this paper to overcome the computer memory shortage problem and to reduce the time cost in basic APC; to reconstruct the source signal by unmixing the mixed fMRI data using the time courses which are derived from the ultimate exemplars.In the task-related experiments, we can see that SAAPC is more accurate to detect the functional networks than basic APC, and it significantly reduces the time cost relative to basic APC. In addition, in the resting-state data experiments, the SAAPC method can successfully identify typical resting-state networks from the resting-state data set, while this performance is seldom reported by the classical cluster method and the basic APC method. This proposed clustering analysis method is expected to have wide applicability.

Handwritten Chinese text editing and recognition system

This paper describes a handwritten Chinese text editing and recognition system that can edit handwritten text and recognize it with a client-server mode. First, the client end samples and redisplays the handwritten text by using digital ink technics, segments handwritten characters, edits them and saves original handwritten information into a self-defined document. The self-defined document saves coordinates of all sampled points of handwriting characters. Second, the server recognizes handwritten document based on the proposed Gabor feature extraction and affinity propagation clustering (GFAP) method, and returns the recognition results to client end. Moreover, the server can also collect the labeled handwritten characters and fine tune the recognizer automatically. Experimental results on HIT-OR3C database show that our handwriting recognition method improves the recognition performance remarkably.

A K-means multivariate approach for clustering independent components from magnetoencephalographic data

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering.

Groupwise Morphometric Analysis Based on High Dimensional Clustering.

In this paper, we propose an efficient groupwise morphometric analysis to characterize morphological variations between healthy and pathological states. The proposed framework extends the work of Baloch [4] in which a manifold for each anatomy was constructed by collecting lossless [transformation, residual] descriptors with various transformation parameters, and the optimal set of transformation parameters was estimated individually by minimizing group variance. However, full parameter exploration is not desired as it can result in transformation leading to inaccurate anatomical models. In addition, a single fixed template introduces a priori bias to subsequent statistical analysis. In order to overcome these limitations, we use an affinity propagation clustering method to find the spatially close cluster center for each subject. Then, a subject is normalized to the template via the cluster center to restrict our analysis only to those descriptors that reflect reasonable warps. In addition, a mean template is selected by finding a cluster center that minimizes the sum of pairwise shape distance to reduce the fixed template bias. Our method is applied to 2D synthetic data and 3D real Cardiac MR Images. Experimental results show improvement in quantifying and localizing shape changes.

IDENTIFICATION OF COMMUNITY STRUCTURE IN COMPLEX NETWORKS USING AFFINITY PROPAGATION CLUSTERING METHOD

Identifying communities in complex networks has recently attracted considerable attention in different fields. The goal of community identification is to cluster vertices of a network into groups, which is the same as clustering in machine learning and data mining domains. A recent proposed clustering method called affinity propagation shows high performance in clustering data sets into groups, and it does not require that the number of clusters be pre-specified. In this paper, based on a new method for calculating similarity between pairs of vertices and a transforming method for a given similarity from likelihood to log-domain, we apply that affinity propagation clustering method to identify communities in complex networks. Extensive simulation results demonstrate that affinity propagation clustering algorithm is very effective for identifying community structures in both computer-generated and real-world network data.

Locality preserving projections plus affinity propagation: a fast method for face recognition

This letter develops a novel and fast framework called APLPP to tackle the face recognition task by combin- ing locality preserving projections LPP with the affinity propagation clustering method. Unlike LPP, which uses all training images to identify a test image, our affinity propaga- tion LPP APLPP uses much fewer images, free of noise, for identification. Thus APLPP is more computationally effi- cient than LPP. Comparative experiments also indicate that APLPP outperforms LPP regarding the recognition rate.

Integrating affinity propagation clustering method with linear discriminant analysis for face recognition

The Fisherface method suffers from the problem of using all training face images to recognize a test face image. To tackle this problem, we propose combining a novel clustering method, affinity propagation (AP), recently reported in the journal Science, with linear discriminant analysis (LDA) to form a new method, AP-LDA, for face recognition. By using AP, a representative face image for each subject can be obtained. Our AP-LDA method uses only these representative face images rather than all training images for recognition. Thus, it is more computationally efficient than Fisherface. Experimental results on several benchmark face databases also show that AP-LDA outperforms Fisherface in terms of recognition rate.