Traditional clustering algorithms model the clustering problem as an optimization task, in which the objective is defined based on minimizing specific metrics. These algorithms are limited to find clusters with convex polytopes. In contrast, density-based clustering algorithms aim at overcoming this limitation and try to partition data objects into meaningful groups that have relatively high density separated by low-density regions. This work describes and evaluates a new density-based clustering algorithm, called neighborhood distance entropy consistency (NDEC), which is able to not only detect clusters of arbitrary size, shape, and density, but also identify outliers. To this end, both local and global densities are considered simultaneously to accurately discover the intrinsic clustering structure. In addition, the consistency of neighborhood distance entropy is used as an important criterion to merge potential subclusters. Experiments on synthetic and real benchmark clustering data sets have demonstrated the efficiency and effectiveness of the NDEC method. Comparisons with k-means, DBSCAN, OPTICS, and density peaks clustering algorithms further show that NDEC can successfully discover natural clusters. Additionally, the utility of NDEC is demonstrated with its application on two real-world problems including brain white matter tracts segmentation using diffusion tensor imaging and characterizing motor unit potential trains extracted from electromyographic signals.

Needle Electromyography, in combination with nerve conduction studies, is the gold standard methodology for assessing the neurophysiologic effects of neuromuscular diseases. Muscle categorization is typically based on visual and auditory assessment of the morphology and activation patterns of its constituent motor units. A procedure which is highly dependent on the skills and level of experience of the examiner. This motivates the development of automated or semi-automated categorization techniques. This paper describes a 2-stage Gaussian mixture model based approach. In the first stage, a muscle is classified as neurogenic or myopathic. The second stage uses a classifier specific to each disease category to confirm or refute the disease involvement. A total of 2556 motor unit potentials sampled from 48 normal, 30 neurogenic and 20 myopathic tibialis anterior muscles were utilized for this study. The proposed approach showed an average accuracy of 91.25%, which is higher than the compared linear and non-linear multi-class schemas. In addition to improved accuracy, the 2-stage approach is more suitable for the muscle categorization, because it has a hierarchical decision structure similar to current clinical practice, and its output can be interpreted as a measure of confidence.

In this paper, we present a design methodology for integrating heterogeneous classifier ensembles by employing a diversity-based hybrid classifier fusion approach, whose aggregator module consists of two classifier combiners, to achieve an improved classification performance for motor unit potential classification during electromyographic (EMG) signal decomposition. Following the so-called overproduce and choose strategy to classifier ensemble combination, the developed system allows the construction of a large set of base classifiers, and then automatically chooses subsets of classifiers to form candidate classifier ensembles for each combiner. The system exploits kappa statistic diversity measure to design classifier teams through estimating the level of agreement between base classifier outputs. The pool of base classifiers consists of different kinds of classifiers: the adaptive certainty-based, the adaptive fuzzy k -NN, and the adaptive matched template filter classifiers; and utilizes different types of features. Performance of the developed system was evaluated using real and simulated EMG signals, and was compared with the performance of the constituent base classifiers. Across the EMG signal datasets used, the developed system had better average classification performance overall, especially in terms of reducing classification errors. For simulated signals of varying intensity, the developed system had an average correct classification rate CCr of 93.8% and an error rate Er of 2.2% compared to 93.6% and 3.2%, respectively, for the best base classifier in the ensemble. For simulated signals with varying amounts of shape and/or firing pattern variability, the developed system had a CCr of 89.1% with an Er of 4.7% compared to 86.3% and 5.6%, respectively, for the best classifier. For real signals, the developed system had a CCr of 89.4% with an Er of 3.9% compared to 84.6% and 7.1%, respectively, for the best classifier.

Rule-based classifiers allow rationalization of classifications made. This in turn improves understanding which is essential for effective decision support. As a rule based classifier, the pattern discovery (PD) algorithm functions well in discrete, nominal and continuous data domains. A drawback when using PD as a classifier for decision support is that it has an unbounded decision space that confounds the understanding of the degree of support for a decision. Incorporating PD into a fuzzy inference system (FIS) allows the the degree of support for a decision to be expressed with intuitively understandable terms. In addition, using discrete algorithms in continuous domains can result in reduced accuracy due to quantization. Fuzzification reduces this ldquocost of quantizationrdquo and improves classification performance. In this work, the PD algorithm was used as a source of rules for a series of FISs implemented using different rule weighting and defuzzification schemes, each providing a linguistic basis for rule description and a bounded space for expression of decision support. The output of each FIS consists of a suggested outcome, a strong confidence metric describing suggestions within this space and a linguistic expression of the rules. This constitutes a stronger basis for decision making than that provided by PD alone. A variety of synthetic, continuous class distributions with varying degrees of separation was used to evaluate the performance of fuzzy, PD, back-propagation and Bayesian classifiers. Overall, the accuracy of the fuzzy system was found to be similar, but slightly below, that of the inherently continuous valued classifiers and was somewhat improved with respect to the PD classifiers. For the difficult spiral class distributions studied, the fuzzy classifiers were able to make more classifications than the PD classifiers. The correct classification rates for the fuzzy classifiers were similar across the various rule weighting and defuzzification schemes, demonstrating the strength of the statistical method for rule generation. Analysis of several real-world data sets shows that a PD-based FIS has comparable performance to a neuro-fuzzy system. The use of a PD based FIS however, provides insight into the structure of the data analyzed not available through the other approaches.

This work presents a system intended to assist in the diagnosis of muscular disease based on the decomposition of electromyographic data through quantitative means. The performance of the system is evaluated both through quantitative analysis of correct suggestions as well as descriptively through an examination of the decision space presented in several pertinent examples. Data used for this analysis is synthetically created gold-standard data produced through a previously described muscle simulator and prepared through application of the DQEMG program. The design of the decision support system is based on a conceptual model reflecting the decision process present in clinical work; the provision of a drill-down exploration mechanism to allow investigation of the statistical support underlying the decision process allows access to the needed statistical confidences required for informed clinical diagnosis while providing summary data display to allow a clinician access to muscle-level characterizations in a reproducible and quantifiable way. The use of the "pattern discovery" algorithm provides a statistically sound mechanism for the generation of rules from a training data set. The exceptional performance observed here provides confidence in the system as a potential tool for use in clinical diagnosis

A framework for the development of a decision support system (DSS) that exhibits uncommonly transparent rule-based inference logic is introduced. A DSS is constructed by marrying a statistically based fuzzy inference system (FIS) with a user interface, allowing drill-down exploration of the underlying statistical support, providing transparent access to both the rule-based inference as well as the underlying statistical basis for the rules. The FIS is constructed through a "pattern discovery" based analysis of training data. Such an analysis yields a rule base characterized by simple explanations for any rule or data division in the extracted knowledge base. The reliability of a fuzzy inference is well predicted by a confidence measure that determines the probability of a correct suggestion by examination of values produced within the inference calculation. The combination of these components provides a means of constructing decision support systems that exhibit a degree of transparency beyond that commonly observed in supervised-learning-based methods. A prototype DSS is analyzed in terms of its workflow and usability, outlining the insight derived through use of the framework. This is demonstrated by considering a simple synthetic data example and a more interesting real-world example application with the goal of characterizing patients with respect to risk of heart disease. Specific input data samples and corresponding output suggestions created by the system are presented and discussed. The means by which the suggestions made by the system may be used in a larger decision context is evaluated

The pattern discovery (PD) algorithm of Wang and Wong was applied as a classifier to several continuous-valued data sets generated to explore performance across a selection of interesting linearly and non-linearly separable class distributions. Performance of several configurations of PD and backpropagation (BP) neural network classifiers and a minimum inter-class distance (MICD) classifier was quantified and compared. The best performance of the PD and BP classifiers were found to be similar for all class distributions studied and close to the optimal IMICD performance for linearly separable class distributions. The performance of both PD and BP classifiers was dependent on the classifier configuration. PD classifier performance depended on the number of intervals used to quantize the continuous data in a predictable, class-distribution independent way. BP performance depended on the number of hidden nodes in a way which was class-distribution dependent and difficult to determine a priori. The transparency and statistical validity of the patterns used and the decisions made by PD classifiers make them highly suitable for problems in which the rationale and confidence of classifications are required so that multiple classifications can be effectively combined to support decisions in a broader context such as medical diagnosis. The strong absolute and relative performance of PD classifiers and the relative simplicity of their implementation when applied to continuous-valued data suggest that they can be effectively utilized in decision support systems in which the underlying data is continuous or discrete valued.