Showing papers by "James C. Bezdek published in 2014"
TL;DR: A model for the analysis of time series sensor data collected at an eldercare facility that builds sets of linguistic summaries from the sensor data that describe different events that may occur each night.
Abstract: We present a model for the analysis of time series sensor data collected at an eldercare facility. The sensors measure restlessness in bed and bedroom motion of residents during the night. Our model builds sets of linguistic summaries from the sensor data that describe different events that may occur each night. A dissimilarity measure produces a distance matrix D between selected sets of summaries. Visual examination of the image of a reordered version of D provides an estimate for the number of clusters to seek in D. Then, clustering with single linkage or non-Euclidean relational fuzzy c-means produces groups of summaries. Subsequently, each group is represented by a linguistic medoid prototype. The prototypes can be used for resident monitoring, two types of anomaly detection, and interresident comparisons. We illustrate our model with real data for two residents collected at TigerPlace: the “aging in place” facility in Columbia, MO, USA.
40 citations
TL;DR: This article compares five methods for Euclideanizing D to D and concludes that the subdominant ultrametric transformation is a clear winner, producing much better partitions of D ˜ than the other four methods.
35 citations
25 Jun 2014
TL;DR: Two new incremental models for online anomaly detection in data streams at nodes in wireless sensor networks are discussed and one of the new models has a mechanism that enables graceful degradation of inputs in the distant past fading memory.
Abstract: Two new incremental models for online anomaly detection in data streams at nodes in wireless sensor networks are discussed. These models are incremental versions of a model that uses ellipsoids to detect first, second, and higher-ordered anomalies in arrears. The incremental versions can also be used this way but have additional capabilities offered by processing data incrementally as they arrive in time. Specifically, they can detect anomalies 'on-the-fly' in near real time. They can also be used to track temporal changes in near real-time because of sensor drift, cyclic variation, or seasonal changes. One of the new models has a mechanism that enables graceful degradation of inputs in the distant past fading memory. Three real datasets from single sensors in deployed environmental monitoring networks are used to illustrate various facets of the new models. Examples compare the incremental version with the previous batch and dynamic models and show that the incremental versions can detect various types of dynamic anomalies in near real time. Copyright © 2012 John Wiley & Sons, Ltd.
29 citations
TL;DR: This paper proposes to improve the estimation of PM concentration by complementing the existing high-precision but expensive PM devices with low-cost lower precision PM sensor nodes, and examines the impact of the precision of the lost-cost sensors on the overall estimation accuracy.
Abstract: Increased levels of particulate matter (PM) in the atmosphere have contributed to an increase in mortality and morbidity in communities and are the main contributing factor for respiratory health problems in the population. Currently, PM concentrations are sparsely monitored; for instance, a region of over 2200 square kilometers surrounding Melbourne in Victoria, Australia, is monitored using ten sensor stations. This paper proposes to improve the estimation of PM concentration by complementing the existing high-precision but expensive PM devices with low-cost lower precision PM sensor nodes. Our evaluation reveals that local PM estimation accuracies improve with higher densities of low-precision sensor nodes. Our analysis examines the impact of the precision of the lost-cost sensors on the overall estimation accuracy.
25 citations
01 Dec 2014
TL;DR: This paper generalizes eight information theoretic crisp indices to soft clusterings, so that they can be used with partitions of any type (i.e., crisp or soft, with soft including fuzzy, probabilistic and possibilistic cases).
Abstract: There have been a large number of external validity indices proposed for cluster validity. One such class of cluster comparison indices is the information theoretic measures, due to their strong mathematical foundation and their ability to detect non-linear relationships. However, they are devised for evaluating crisp (hard) partitions. In this paper, we generalize eight information theoretic crisp indices to soft clusterings, so that they can be used with partitions of any type (i.e., crisp or soft, with soft including fuzzy, probabilistic and possibilistic cases). We present experimental results to demonstrate the effectiveness of the generalized information theoretic indices.
14 citations
06 Jul 2014
TL;DR: A new method for multidimensional scaling in dissimilarity data that is based on preservation of metric topology between the original and derived data sets and produces feature vector realizations that compare favorably with the other approaches on three real relational data sets.
Abstract: We introduce a new method for multidimensional scaling in dissimilarity data that is based on preservation of metric topology between the original and derived data sets. The model seeks neighbors in the derived data that have the same ranks as in the input data. The algorithm we use to optimize the model is a modification of particle swarm optimization called multiswarming. We compare the new method to three well known approaches: Principal component analysis, Sammon's method, and (Kruskal's) metric MDS. Our method produces feature vector realizations that compare favorably with the other approaches on three real relational data sets.