1,558 research outputs found

    AN UNSUPERVISED KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM WITH KERNEL NORMALISATION

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    In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios. </jats:p

    Techniques for clustering gene expression data

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    Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered

    Deep Representation-aligned Graph Multi-view Clustering for Limited Labeled Multi-modal Health Data

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    Today, many fields are characterised by having extensive quantities of data from a wide range of dissimilar sources and domains. One such field is medicine, in which data contain exhaustive combinations of spatial, temporal, linear, and relational data. Often lacking expert-assessed labels, much of this data would require analysis within the fields of unsupervised or semi-supervised learning. Thus, reasoned by the notion that higher view-counts provide more ways to recognise commonality across views, contrastive multi-view clustering may be utilised to train a model to suppress redundancy and otherwise medically irrelevant information. Yet, standard multi-view clustering approaches do not account for relational graph data. Recent developments aim to solve this by utilising various graph operations including graph-based attention. And within deep-learning graph-based multi-view clustering on a sole view-invariant affinity graph, representation alignment remains unexplored. We introduce Deep Representation-Aligned Graph Multi-View Clustering (DRAGMVC), a novel attention-based graph multi-view clustering model. Comparing maximal performance, our model surpassed the state-of-the-art in eleven out of twelve metrics on Cora, CiteSeer, and PubMed. The model considers view alignment on a sample-level by employing contrastive loss and relational data through a novel take on graph attention embeddings in which we use a Markov chain prior to increase the receptive field of each layer. For clustering, a graph-induced DDC module is used. GraphSAINT sampling is implemented to control our mini-batch space to capitalise on our Markov prior. Additionally, we present the MIMIC pleural effusion graph multi-modal dataset, consisting of two modalities registering 3520 chest X-ray images along with two static views registered within a one-day time frame: vital signs and lab tests. These making up the, in total, three views of the dataset. We note a significant improvement in terms of separability, view mixing, and clustering performance comparing DRAGMVC to preceding non-graph multi-view clustering models, suggesting a possible, largely unexplored use case of unsupervised graph multi-view clustering on graph-induced, multi-modal, and complex medical data

    Image similarity in medical images

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    Recent experiments have indicated a strong influence of the substrate grain orientation on the self-ordering in anodic porous alumina. Anodic porous alumina with straight pore channels grown in a stable, self-ordered manner is formed on (001) oriented Al grain, while disordered porous pattern is formed on (101) oriented Al grain with tilted pore channels growing in an unstable manner. In this work, numerical simulation of the pore growth process is carried out to understand this phenomenon. The rate-determining step of the oxide growth is assumed to be the Cabrera-Mott barrier at the oxide/electrolyte (o/e) interface, while the substrate is assumed to determine the ratio β between the ionization and oxidation reactions at the metal/oxide (m/o) interface. By numerically solving the electric field inside a growing porous alumina during anodization, the migration rates of the ions and hence the evolution of the o/e and m/o interfaces are computed. The simulated results show that pore growth is more stable when β is higher. A higher β corresponds to more Al ionized and migrating away from the m/o interface rather than being oxidized, and hence a higher retained O:Al ratio in the oxide. Experimentally measured oxygen content in the self-ordered porous alumina on (001) Al is indeed found to be about 3% higher than that in the disordered alumina on (101) Al, in agreement with the theoretical prediction. The results, therefore, suggest that ionization on (001) Al substrate is relatively easier than on (101) Al, and this leads to the more stable growth of the pore channels on (001) Al

    Image similarity in medical images

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    Organising a photograph collection based on human appearance

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    This thesis describes a complete framework for organising digital photographs in an unsupervised manner, based on the appearance of people captured in the photographs. Organising a collection of photographs manually, especially providing the identities of people captured in photographs, is a time consuming task. Unsupervised grouping of images containing similar persons makes annotating names easier (as a group of images can be named at once) and enables quick search based on query by example. The full process of unsupervised clustering is discussed in this thesis. Methods for locating facial components are discussed and a technique based on colour image segmentation is proposed and tested. Additionally a method based on the Principal Component Analysis template is tested, too. These provide eye locations required for acquiring a normalised facial image. This image is then preprocessed by a histogram equalisation and feathering, and the features of MPEG-7 face recognition descriptor are extracted. A distance measure proposed in the MPEG-7 standard is used as a similarity measure. Three approaches to grouping that use only face recognition features for clustering are analysed. These are modified k-means, single-link and a method based on a nearest neighbour classifier. The nearest neighbour-based technique is chosen for further experiments with fusing information from several sources. These sources are context-based such as events (party, trip, holidays), the ownership of photographs, and content-based such as information about the colour and texture of the bodies of humans appearing in photographs. Two techniques are proposed for fusing event and ownership (user) information with the face recognition features: a Transferable Belief Model (TBM) and three level clustering. The three level clustering is carried out at “event” level, “user” level and “collection” level. The latter technique proves to be most efficient. For combining body information with the face recognition features, three probabilistic fusion methods are tested. These are the average sum, the generalised product and the maximum rule. Combinations are tested within events and within user collections. This work concludes with a brief discussion on extraction of key images for a representation of each cluster

    Anisotropic mean shift based fuzzy c-means segmentation of deroscopy images

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    Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions

    Unsupervised learning methods for identifying and evaluating disease clusters in electronic health records

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    Introduction Clustering algorithms are a class of algorithms that can discover groups of observations in complex data and are often used to identify subtypes of heterogeneous diseases in electronic health records (EHR). Evaluating clustering experiments for biological and clinical significance is a vital but challenging task due to the lack of consensus on best practices. As a result, the translation of findings from clustering experiments to clinical practice is limited. Aim The aim of this thesis was to investigate and evaluate approaches that enable the evaluation of clustering experiments using EHR. Methods We conducted a scoping review of clustering studies in EHR to identify common evaluation approaches. We systematically investigated the performance of the identified approaches using a cohort of Alzheimer's Disease (AD) patients as an exemplar comparing four different clustering methods (K-means, Kernel K-means, Affinity Propagation and Latent Class Analysis.). Using the same population, we developed and evaluated a method (MCHAMMER) that tested whether clusterable structures exist in EHR. To develop this method we tested several cluster validation indexes and methods of generating null data to see which are the best at discovering clusters. In order to enable the robust benchmarking of evaluation approaches, we created a tool that generated synthetic EHR data that contain known cluster labels across a range of clustering scenarios. Results Across 67 EHR clustering studies, the most popular internal evaluation metric was comparing cluster results across multiple algorithms (30% of studies). We examined this approach conducting a clustering experiment on AD patients using a population of 10,065 AD patients and 21 demographic, symptom and comorbidity features. K-means found 5 clusters, Kernel K means found 2 clusters, Affinity propagation found 5 and latent class analysis found 6. K-means 4 was found to have the best clustering solution with the highest silhouette score (0.19) and was more predictive of outcomes. The five clusters found were: typical AD (n=2026), non-typical AD (n=1640), cardiovascular disease cluster (n=686), a cancer cluster (n=1710) and a cluster of mental health issues, smoking and early disease onset (n=1528), which has been found in previous research as well as in the results of other clustering methods. We created a synthetic data generation tool which allows for the generation of realistic EHR clusters that can vary in separation and number of noise variables to alter the difficulty of the clustering problem. We found that decreasing cluster separation did increase cluster difficulty significantly whereas noise variables increased cluster difficulty but not significantly. To develop the tool to assess clusters existence we tested different methods of null dataset generation and cluster validation indices, the best performing null dataset method was the min max method and the best performing indices we Calinksi Harabasz index which had an accuracy of 94%, Davies Bouldin index (97%) silhouette score ( 93%) and BWC index (90%). We further found that when clusters were identified using the Calinski Harabasz index they were more likely to have significantly different outcomes between clusters. Lastly we repeated the initial clustering experiment, comparing 10 different pre-processing methods. The three best performing methods were RBF kernel (2 clusters), MCA (4 clusters) and MCA and PCA (6 clusters). The MCA approach gave the best results highest silhouette score (0.23) and meaningful clusters, producing 4 clusters; heart and circulatory( n=1379), early onset mental health (n=1761), male cluster with memory loss (n = 1823), female with more problem (n=2244). Conclusion We have developed and tested a series of methods and tools to enable the evaluation of EHR clustering experiments. We developed and proposed a novel cluster evaluation metric and provided a tool for benchmarking evaluation approaches in synthetic but realistic EHR

    Rough set theory applied to pattern recognition of partial discharge in noise affected cable data

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    This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data
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