Proposed Clustering Procedure Research Articles

Fuzzy clustering of time series with time-varying memory

Little attention has been devoted to the long memory among the different data features considered for clustering time series. Following previous literature, we measure the long memory of a time series through the estimated Hurst exponent. However, we exploit the fact that a constant value for the Hurst exponent h is unrealistic in many practical examples. In this paper, assuming that the time series follows a multifractional Brownian motion, we estimate a time-varying Hurst exponent used as the input for a fuzzy clustering procedure. Motivated by the relevance of long memory in finance, the usefulness of the proposed clustering procedure is shown with an application to stock prices.

Multiway clustering with time-varying parameters

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data.

Point-Set Kernel Clustering

Measuring similarity between two objects is the core operation in existing clustering algorithms in grouping similar objects into clusters. This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects. The proposed clustering procedure utilizes this new measure to characterize every cluster grown from a seed object. We show that the new clustering procedure is both effective and efficient that enables it to deal with large scale datasets. In contrast, existing clustering algorithms are either efficient or effective. In comparison with the state-of-the-art density-peak clustering and scalable kernel k-means clustering, we show that the proposed algorithm is more effective and runs orders of magnitude faster when applying to datasets of millions of data points, on a commonly used computing machine.

Graph theory based transformation of existing Distribution network into clusters of multiple micro-grids for reliability enhancement

Enhancement of reliability is critical in designing and planning of distribution systems that operate efficiently and with minimal interruption to customer loads, because they use various types of resources and technologies to meet energy demand such as distributed generation (DG). DG is expected to play an enhanced role in emerging power systems. One of the major issues in the planning stage for future distribution systems is the optimal design of multiple micro-grids (MMGs) in distribution systems. This paper describes a novel method for clustering a traditional passive distribution system into a cluster of Multiple Micro Grids (MMGs). Clustering a traditional distribution system into MMGs provides numerous merits for costumers and distribution system operators, including a local control strategy to minimise interaction between various MGs, local reactive power compensation to prevent fault propagation, minimise power losses, and ultimately improving system reliability. In each MG area, DGs and reactive power resources are deployed to reach the acceptable level of reliability. The proposed clustering procedure is based on a weighted graph partitioning approach, with the weights representing the apparent power of the lines. Various reliability tests were performed in order to determine the best location for DG penetration in the distribution system. The passive distribution system becomes an active system after DG penetration. As a result, planners must investigate the influence of DG on distribution system reliability. The aim of this paper is to investigate the impact of DG on distribution system reliability. The results on the test distribution system demonstrate the proposed method’s performance and effectiveness.

Robust weighted K-means clustering algorithm for a probabilistic-shaped 64QAM coherent optical communication system.

A novel weighted K-means scheme for a probabilistic-shaped (PS) 64 quadrature amplitude modulation (QAM) signal is proposed in order to locate the decision points more accurately and enhance the robustness of clustering algorithm. By using a weighting factor following the reciprocal of Maxwell-Boltzmann distribution, the proposed algorithm can combine the advantages of PS and K-means robustly while reducing the overall computational complexity of the clustering process. Experimental verification of the proposed clustering technique was demonstrated in a 120-Gb/s probabilistic-shaped 64QAM coherent optical communication system. The results show that the proposed algorithm has outperformed K-means with respect to bit error rate (BER), clustering robustness and iteration times in both back-to-back and 375km transmission scenarios. For the back-to-back situation, the proposed algorithm is capable of achieving about 0.6dB and 1.8dB OSNR gain over K-means clustered signals and unclustered signals. For the case of transmission, the proposed clustering procedure can robustly locate the optimal decision points with launched signal power ranging from -5dBm to 5dBm, while the working range for K-means procedure is only -4dBm to 2dBm. In addition, the proposed weighted algorithm takes less iteration times than K-means to converge, especially when the signal impairments caused by fiber Kerr nonlinearity is severe.

New similarity measures of intuitionistic fuzzy sets based on the Jaccard index with its application to clustering

A similarity measure is a useful tool for determining the similarity between two objects. Although there are many different similarity measures among the intuitionistic fuzzy sets (IFSs) proposed in the literature, the Jaccard index has yet to be considered as way to define them. The Jaccard index is a statistic used for comparing the similarity and diversity of sample sets. In this study, we propose a new similarity measure for IFSs induced by the Jaccard index. According to our results, proposed similarity measures between IFSs based on the Jaccard index present better properties. Several examples are used to compare the proposed approach with several existing methods. Numerical results show that the proposed measures are more reasonable than these existing measures. On the other hand, measuring the similarity between IFSs is also important in clustering. Thus, we also propose a clustering procedure by combining the proposed similarity measure with a robust clustering method for analyzing IFS data sets. We also compare the proposed clustering procedure with two clustering methods for IFS data sets.

Clustering of financial instruments using jump tail dependence coefficient

In this paper, we propose a new clustering procedure for financial instruments. Unlike the prevalent clustering procedures based on time series analysis, our procedure employs the jump tail dependence coefficient as the dissimilarity measure, assuming that the observed logarithm of the prices/indices of the financial instruments are embedded into multidimensional Levy processes. The efficiency of our proposed clustering procedure is tested by a simulation study. Finally, with the help of the real data of country indices we illustrate that our clustering procedure could help investors avoid potential huge losses when constructing portfolios.

HIGHLY DISTRIBUTED AND ENERGY EFFICIENT CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS

Wireless sensor network (WSN) is a low-powered prestigious network fashioned by sensor nodes that treasures application in civilian, military, visual sense models and many others. Reduced energy utilization is an exigent task for these sensor networks. By the data aggregation procedure, needless communication between sensor nodes, cluster head and the base station is eluded. An evaluation of energy efficient optical low energy adaptive clustering hierarchy has been performed and the enactments have been compared with the prevailing low energy adaptive clustering hierarchy algorithm, between two detached wireless sensor network fields. The proposed clustering procedure has been primarily implemented to join two distinct wireless sensor fields. An optical fiber is used to join two reserved wireless sensor fields. This distributed clustering methodology chiefly targets in exploiting the parameters like network lifetime, throughput and energy efficiency of the whole wireless sensor system.

HIGHLY DISTRIBUTED AND ENERGY EFFICIENT CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS

Wireless sensor network (WSN) is a low-powered prestigious network fashioned by sensor nodes that treasures application in civilian, military, visual sense models and many others. Reduced energy utilization is an exigent task for these sensor networks. By the data aggregation procedure, needless communication between sensor nodes, cluster head and the base station is eluded. An evaluation of energy efficient optical low energy adaptive clustering hierarchy has been performed and the enactments have been compared with the prevailing low energy adaptive clustering hierarchy algorithm, between two detached wireless sensor network fields. The proposed clustering procedure has been primarily implemented to join two distinct wireless sensor fields. An optical fiber is used to join two reserved wireless sensor fields. This distributed clustering methodology chiefly targets in exploiting the parameters like network lifetime, throughput and energy efficiency of the whole wireless sensor system.

Employing heat maps to mine associations in structured routine care data

Mining the electronic medical record (EMR) has the potential to deliver new medical knowledge about causal effects, which are hidden in statistical associations between different patient attributes. It is our goal to detect such causal mechanisms within current research projects which include e.g. the detection of determinants of imminent ICU readmission. An iterative statistical approach to examine each set of considered attribute pairs delivers potential answers but is difficult to interpret. Therefore, we aimed to improve the interpretation of the resulting matrices by the use of heat maps. We propose strategies to adapt heat maps for the search for associations and causal effects within routine EMR data. Heat maps visualize tabulated metric datasets as grid-like choropleth maps, and thus present measures of association between numerous attribute pairs clearly arranged. Basic assumptions about plausible exposures and outcomes are used to allocate distinct attribute sets to both matrix dimensions. The image then avoids certain redundant graphical elements and provides a clearer picture of the supposed associations. Specific color schemes have been chosen to incorporate preexisting information about similarities between attributes. The use of measures of association as a clustering input has been taken as a trigger to apply transformations which ensure that distance metrics always assume finite values and treat positive and negative associations in the same way. To evaluate the general capability of the approach, we conducted analyses of simulated datasets and assessed diagnostic and procedural codes in a large routine care dataset. Simulation results demonstrate that the proposed clustering procedure rearranges attributes similar to simulated statistical associations. Thus, heat maps are an excellent tool to indicate whether associations concern the same attributes or different ones, and whether affected attribute sets conform to any preexisting relationship between attributes. The dendrograms help in deciding if contiguous sequences of attributes effectively correspond to homogeneous attribute associations. The exemplary analysis of a routine care dataset revealed patterns of associations that follow plausible medical constellations for several diseases and the associated medical procedures and activities. Cases with breast cancer (ICD C50), for example, appeared to be associated with radiation therapy (8-52). In cross check, approximately 60 percent of the attribute pairs in this dataset showed a strong negative association, which can be explained by diseases treated in a medical specialty which routinely does not perform the respective procedures in these cases. The corresponding diagram clearly reflects these relationships in the shape of coherent subareas. We could demonstrate that heat maps of measures of association are effective for the visualization of patterns in routine care EMRs. The adjustable method for the assignment of attributes to image dimensions permits a balance between the display of ample information and a favorable level of graphical complexity. The scope of the search can be adapted by the use of pre-existing assumptions about plausible effects to select exposure and outcome attributes. Thus, the proposed method promises to simplify the detection of undiscovered causal effects within routine EMR data.

Dynamic clustering of histogram data based on adaptive squared Wasserstein distances

This paper presents a Dynamic Clustering Algorithm for histogram data with an automatic weighting step of the variables by using adaptive distances. The Dynamic Clustering Algorithm is a k-means-like algorithm for clustering a set of objects into a predefined number of classes. Histogram data are realizations of particular set-valued descriptors defined in the context of Symbolic Data Analysis. We propose to use the ℓ2 Wasserstein distance for clustering histogram data and two novel adaptive distance based clustering schemes. The ℓ2 Wasserstein distance allows to express the variability of a set of histograms in two components: the first related to the variability of their averages and the second to the variability of the histograms related to different size and shape. The weighting step aims to take into account global and local adaptive distances as well as two components of the variability of a set of histograms. To evaluate the clustering results, we extend some classic partition quality indexes when the proposed adaptive distances are used in the clustering criterion function. Examples on synthetic and real-world datasets corroborate the proposed clustering procedure.

On-line adaptive clustering for process monitoring and fault detection

An adaptive clustering procedure specifically designed for process monitoring, fault detection and isolation is presented in this paper. The key feature of the proposed procedure can be identified as its underlying capability to detect novelties in the system’s mode of operation and, thus, to identify previously unseen functioning modes of the process. Once a novelty is detected, relevant informations are used to enrich the knowledge-base of the algorithm and as a result the proposed clustering procedure evolves and learns the new features of the monitored process in accordance with the available process data. The suggested clustering procedure is theoretically illustrated and its effectiveness has been investigated experimentally. Particularly, the on-line implementation of the algorithm and its integration with a fault detection expert system have been considered by making reference to a pneumatic process.

An effective clustering procedure of neuronal response profiles in graded thermal stimulation

Functional data analysis has gained significant attention from a variety of disciplines. In the present study we propose an effective clustering procedure to categorize a number of profiles that are formed with nonlinear functions. The proposed clustering procedure first smoothes the data and then transforms the smoothed data to obtain their functional form. The coefficients of the function obtained from the preceding transformation step are used for clustering. Simulation studies under various scenarios indicated that our proposed clustering procedure correctly identified the true clusters and yielded better clustering results than a latent class cluster analysis, one of the existing clustering methods. Furthermore, the effectiveness of the proposed clustering procedure was demonstrated using real pain data in which the main objective is to characterize the responses of 144 spinal cord dorsal horn neurons with graded thermal stimuli that range from 37 °C to 51 °C in 2 °C increments. The results showed that the proposed clustering strategy can successfully elicit natural grouping of the neurons with similar response patterns to graded thermal stimuli.

Adaptive Mean-Linkage with Penalty: A new algorithm for cluster analysis

A novel cluster analysis technique, so-called Adaptive Mean-Linkage with Penalty algorithm (AMLP) is proposed. The method is based on a penalty concept applied to the Euclidian distance, which determines the dissimilarity among objects when clustering data. The implementation of this technique is straightforward and provides enhanced classification in our case studies. The proposed clustering procedure was applied to a dataset of compounds from the essential oil of plants acquired for classification purpose. The potentiality of this novel technique to distinguish each plant or group of plants according to the concentration levels of compounds in the essential oil has been validated. A free web tool is available.

A Kth Nearest Neighbour Clustering Procedure

Summary Due to the lack of development in the probabilistic and statistical aspects of clustering research, clustering procedures are often regarded as heuristics generating artificial clusters from a given set of sample data. In this paper, a clustering procedure that is useful for drawing statistical inference about the underlying population from a random sample is developed. It is based on the uniformly consistent kth nearest neighbour density estimate, and is applicable to both case-by-variable data matrices and case-by-case dissimilarity matrices. The proposed clustering procedure is shown to be asymptotically consistent for high-density clusters in several dimensions, and its small-sample behaviour is illustrated by an empirical example.