A signal interpretation engine apparatus and method are disclosed as including a computer programmed to run a feature expansion module, a weight table module, a consolidation module, and a map generation module. The feature expansion module contains feature operators for operating on a signal to expand the signal to form a feature map of feature segments. Each feature segment corresponds to a unique representation of the signal created by a feature operator from the signal across an epoch. An epoch corresponds to an event within a time segment. A consolidation module applies aggregators to consolidate the inner products or superposition segments into a distribution function representing an attribute over a domain reflecting a selected weight table, aggregator, and event type, corresponding to each value of the attribute.

Signal interpretation engine

In many knowledge discovery and data mining tasks, fuzzy clustering is one of the most common tools for data partitioning. In this paper dynamic fuzzy clustering models for classifying a set of multivariate time trajectories (time series, sequences) are developed. In particular, by adopting an exploratory approach, based on a geometric-algebraic formulation of the data time array, different kinds of dynamic fuzzy clustering models, based on cross sectional and longitudinal aspects, are suggested. Furthermore, a modified version of the previous clustering models, that can be seen as a generalization of these models, is proposed. By utilizing these models we can obtain beneficial effects in the clustering process when anomalous trajectories (trajectories with anomalous positions and slopes) are present in the dataset; in fact the models are suitable for detecting structures of time trajectories with anomalous patterns that are not uniformly distributed over the structure's domains and are characterized by strange slopes. In these models, the disruptive effect of the anomalous trajectories is neutralized and smoothed and the information on the influence of individual time trajectories on the detected groups is given. Furthermore, some remarks on dynamic three-way extensions of a few robust fuzzy clustering models for two-way data are suggested. Demonstrative examples are shown and a comparison assessment based on artificial multivariate time-varying data is carried out

Fuzzy Clustering for Data Time Arrays With Inlier and Outlier Time Trajectories

A method for assessing a condition of an organism having body waves corresponding to states of the organism. The method includes the step of recording signals corresponding to a body wave, output by a portion of the organism in a first state, to provide a first record. In a similar manner, signals are recorded during a time period in which the organism is in a second state, to provide a second record. The first and second records, are processed by applying feature expansion procedures thereto. The results of the feature expansion procedures are evaluated to identify first selected feature expansion procedures effective to distinguish values of the first signals corresponding to the first state from values of the first signals corresponding to the second state.

Drug profiling apparatus and method

The classification of multivariate time-varying data finds application in several fields, such as economics, finance, marketing research, psychometrics, bioinformatics, medicine, signal processing,...

Fuzzy c-means clustering models for multivariate time-varying data: Different approaches

A method for predicting the state of a geological formation. The method may include generating a separation key effective to extract a first feature from a signal or signals corresponding to a first state and a second feature, distinct from the first feature, from a signal or signals corresponding to a second state. The separation key may list at least one feature operator and a weighting table. The at least one feature operator may expand a test signal collected from a geological formation of unknown state in at least one of frequency space and time space to generate a plurality of feature segments. A weighting table may weight the plurality of feature segments. The weighted plurality of feature segments may be superimposed to form a third feature. The geological formation may be classified as having one of the first state and second state based on the correspondence of the third feature to one of the first feature and second feature.

J. J. Rajan

Papers

Unsupervised time series classification