Online pattern recognition in subsequence time series clustering

One of the open issues in the context of subsequence time series clustering is online pattern recognition. There are different fields in this clustering such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. Among these fields pattern recognition is one the essential concept. To implement the idea of online pattern recognition, we choose sequences of ECG data as a subsequence time series data. Additionally, using ECG data can help to interpret heart activity for finding heart diseases. This paper will offer a way to generate online pattern recognition in subsequence time series clustering in order to have a runtime results

Dynamic clustering of time series with Echo State Networks

In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Time Series Clustering with Deep Reservoir Computing

This paper proposes a method for clustering of time series, based upon the ability of deep Reservoir Computing networks to grasp the dynamical structure of the series that is presented as input. A standard clustering algorithm, such as k-means, is applied to the network states, rather than the input series themselves. Clustering is thus embedded into the network dynamical evolution, since a clustering result is obtained at every time step, which in turn serves as initialisation at the next step. We empirically assess the performance of deep reservoir systems in time series clustering on benchmark datasets, considering the influence of crucial hyperparameters. Experimentation with the proposed model shows enhanced clustering quality, measured by the silhouette coefficient, when compared to both static clustering of data, and dynamic clustering with a shallow network

Routine Modeling with Time Series Metric Learning

version éditeur : https://rd.springer.com/chapter/10.1007/978-3-030-30484-3_47International audienceTraditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities. This work proposes to recognize recurrent activity patterns, called routines, instead of precisely defined activities. The modeling of routines is defined as a metric learning problem, and an architecture, called SS2S, based on sequence-to-sequence models is proposed to learn a distance between time series. This approach only relies on inertial data and is thus non intrusive and preserves privacy. Experimental results show that a clustering algorithm provided with the learned distance is able to recover daily routines

A scalable analytical framework for spatio-temporal analysis of neighborhood change: A sequence analysis approach

© Springer Nature Switzerland AG 2020. Spatio-temporal changes reflect the complexity and evolution of demographic and socio-economic processes. Changes in the spatial distribution of population and consumer demand at urban and rural areas are expected to trigger changes in future housing and infrastructure needs. This paper presents a scalable analytical framework for understanding spatio-temporal population change, using a sequence analysis approach. This paper uses gridded cell Census data for Great Britain from 1971 to 2011 with 10-year intervals, creating neighborhood typologies for each Census year. These typologies are then used to analyze transitions of grid cells between different types of neighborhoods and define representative trajectories of neighborhood change. The results reveal seven prevalent trajectories of neighborhood change across Great Britain, identifying neighborhoods which have experienced stable, upward and downward pathways through the national socioeconomic hierarchy over the last four decades

Unsupervised Visual Time-Series Representation Learning and Clustering

Time-series data is generated ubiquitously from Internet-of-Things (IoT) infrastructure, connected and wearable devices, remote sensing, autonomous driving research and, audio-video communications, in enormous volumes. This paper investigates the potential of unsupervised representation learning for these time-series. In this paper, we use a novel data transformation along with novel unsupervised learning regime to transfer the learning from other domains to time-series where the former have extensive models heavily trained on very large labelled datasets. We conduct extensive experiments to demonstrate the potential of the proposed approach through time-series clustering. Source code available at https://github.com/technophyte/LDVR.</p

Frequency distribution of HPV18 based on the detection of E6 oncoprotein gene in cervix cancer samples

Background: Persistent infection with high-risk human papillomavirus (HPV) is one of the most important risk factors for developing cervix cancer. Since cell culture and serological methods have no diagnostic value for the detection of this virus and its variants, the importance of molecular methods such as PCR in the early and definite diagnosis of such virus becomes evident. This study aimed to evaluate the frequency of HPV18 based on detecting E6 gene in paraffin block samples using the PCR method. Materials and Methods: In this study, 69 out of 150 cervix samples of precancerous and cancerous lesions were collected during 2007-2012. DNA was extracted from paraffin blocks using the phenol/chloroform method. Two L1 and E6 consensus primers were used to evaluate the HPV and 18 HPV, respectively. Results: Among 69 patients with cervix cancer, 53 (76.8) cases were HPV-positive and 16 (23.19) HPV-negative. Twelve out of 53 (17.39) HPV-positive cases were HPV18- positive. Moreover, 6 cases were diagnosed with cervical intraepithelial neoplasia II, III and 6 with squamous cell carcinoma. Conclusion: Results of the study confirm the previous reports concerning the relationship between HPV and cervix cancer. Considering the efficiency of DNA extraction and PCR protocol, we can use the test in pathology labs with simple and inexpensive facilities