On-line evolving fuzzy clustering

In this paper, a novel on-line evolving fuzzy clustering method that extends the evolving clustering method (ECM) of Kasabov and Song (2002) is presented, called EFCM. Since it is an on-line algorithm, the fuzzy membership matrix of the data is updated whenever the existing cluster expands, or a new cluster is formed. EFCM does not need the numbers of the clusters to be pre-defined. The algorithm is tested on several benchmark data sets, such as Iris, Wine, Glass, E-Coli, Yeast and Italian Olive oils. EFCM results in the least objective function value compared to the ECM and Fuzzy C-Means. It is significantly faster (by several orders of magnitude) than any of the off-line batch-mode clustering algorithms. A methodology is also proposed for using theXie-Beni cluster validity measure to optimize the number of clusters. © 2007 IEEE

When an initiative promises more than it delivers: a multi-actor perspective of rural entrepreneurship difficulties and failure in Thailand

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.National governments invest in initiatives aimed at encouraging rural entrepreneurship on the assumption that it contributes to competitiveness and employment. Empirical findings about one such initiative in Thailand reveal the nature of entrepreneurship difficulties and the diverse expressions of entrepreneurship failure, not only in the sense of termination of activities and exit but also entrepreneurs’ inability to meet the objectives and aims of the initiative. Significant attitudinal inadequacies such as risk aversion, passivity and over-reliance on the public sector complement entrepreneurship resource weaknesses in explaining rural entrepreneurship difficulties and failure. Findings demonstrate inadequacies of one-size-fits-all policies seeking to encourage rural entrepreneurship by failing to address the needs and capabilities of the involved entrepreneurs. The discussion also extends current research, first, by studying rural entrepreneurship within an institutional framework in an emerging market context; second, by conceptualizing rural entrepreneurship failure and attitudinal drivers of such failure; third, by documenting and analysing the nature, sources and consequences of the distinct constructs of ‘rural entrepreneurship difficulties’ and ‘rural entrepreneurship failure’; and finally, by presenting a revised theorization of ‘failure’ in entrepreneurship research which recognizes the diverse forms that failure may assume

Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data

Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little researched if at all. Especially when patterns of changes (events) in the data across space and time have to be captured and understood. The paper presents novel methods for clustering of spatiotemporal data using the NeuCube spiking neural network (SNN) architecture. Clusters of spatiotemporal data were created and modified on-line in a continuous, incremental way, where spatiotemporal relationships of changes in variables are incrementally learned in a 3D SNN model and the model connectivity and spiking activity are incrementally clustered. Two clustering methods were proposed for SNN, one performed during unsupervised and one—during supervised learning models. Before submitted to the models, the data is encoded as spike trains, a spike representing a change in the variable value (an event). During the unsupervised learning, the cluster centres were predefined by the spatial locations of the input data variables in a 3D SNN model. Then clusters are evolving during the learning, i.e. they are adapted continuously over time reflecting the dynamics of the changes in the data. In the supervised learning, clusters represent the dynamic sequence of neuron spiking activities in a trained SNN model, specific for a particular class of data or for an individual instance. We illustrate the proposed clustering method on a real case study of spatiotemporal EEG data, recorded from three groups of subjects during a cognitive task. The clusters were referred back to the brain data for a better understanding of the data and the processes that generated it. The cluster analysis allowed to discover and understand differences on temporal sequences and spatial involvement of brain regions in response to a cognitive task

Personalised modelling with spiking neural networks integrating temporal and static information.

This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual

From von Neumann architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications

Spatio/Spector-Temporal Data (SSTD) analyzing is a challenging task, as temporal features may manifest complex interactions that may also change over time. Making use of suitable models that can capture the “hidden” interactions and interrelationship among multivariate data, is vital in SSTD investigation. This chapter describes a number of prominent applications built using the Kasabov’s NeuCube-based Spiking Neural Network (SNN) architecture for mapping, learning, visualization, classification/regression and better understanding and interpretation of SSTD

Design of MRI Structured Spiking Neural Networks and Learning Algorithms for Personalized Modelling, Analysis, and Prediction of EEG Signals

Abstract This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others