A Review of Subsequence Time Series Clustering

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies

Clustering the Occupant Behavior in Residential Buildings : a Method Comparison

The aim of this paper is to investigate possible patterns of the occupant behaviour in residential buildings. Measurements were taken in multifamily buildings where several occupantrelated variables were recorded. We chose and compared two different clustering methods: whole time series and features clustering (kmeans algorithm). The mentioned methods were performed selecting two variables (window opening and indoor temperature), and tested with supervised learning methods. Results suggest that features clustering can perform better than whole time series. The representation of the occupant behaviour through features is meant to be applied in future work regarding the optimization of control strategies in ventilation systems

A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals

Dimensionality reduction (feature selection) is an important step in pattern recognition systems. Although there are different conventional approaches for feature selection, such as Principal Component Analysis, Random Projection, and Linear Discriminant Analysis, selecting optimal, effective, and robust features is usually a difficult task. In this paper, a new two-stage approach for dimensionality reduction is proposed. This method is based on one-dimensional and two-dimensional spectrum diagrams of standard deviation and minimum to maximum distributions for initial feature vector elements. The proposed algorithm is validated in an OCR application, by using two big standard benchmark handwritten OCR datasets, MNIST and Hoda. In the beginning, a 133-element feature vector was selected from the most used features, proposed in the literature. Finally, the size of initial feature vector was reduced from 100% to 59.40% (79 elements) for the MNIST dataset, and to 43.61% (58 elements) for the Hoda dataset, in order. Meanwhile, the accuracies of OCR systems are enhanced 2.95% for the MNIST dataset, and 4.71% for the Hoda dataset. The achieved results show an improvement in the precision of the system in comparison to the rival approaches, Principal Component Analysis and Random Projection. The proposed technique can also be useful for generating decision rules in a pattern recognition system using rule-based classifiers

Komparativna analiza profesionalne naobrazbe i projekata krajobrazne arhitekture u Iranu

This paper studies the performance of contemporary Iranian landscape architecture in the two areas, namely that of education and professional projects. It is divided into two sections; the first section being based on the qualitative method of grounded theory which analyzes theories by coding the concepts. The second section uses a comparative approach, whereby the criterion extracted from the first section is discussed in the two aforementioned areas of contemporary Iranian landscape architecture.Ovaj se rad bavi istraživanjem suvremene iranske perivojne arhitekture iz dvije perspektive: obrazovanja s jedne strane i stručnih projekata s druge strane. Rad je strukturiran u dvije cjeline od kojih se prva bavi kvalitativnom metodom utemeljene teorije kojom se analiziraju teorije putem kodiranja koncepata. U drugom se dijelu rada komparativnim pristupom analizira kriterij dobiven u prvome dijelu u njegovoj primjeni na spomenuta dva područja suvremene iranske perivojne arhitekture

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

A Study on Perceived Discrimination from Implementation of the Health Reform Program among Employees of Training Organizations in Rafsanjan City, 2015

Background: After security, the second most important human need is health and the healthcare reform plan is a program for improving health services. The purpose of this study was to investigate the social consequences of implementing the health system reform plan in Rafsanjan, with emphasis on the feeling of discrimination among employees of the educational organizations of this city. Methods: In this descriptive and analytical cross-sectional study, 362 employees of the educational organizations were selected through stratified random sampling in 2015. A researcher-made questionnaire with 21 questions was used to measure the variables. One-way analysis of variance and Pearson correlation coefficient were used for statistical analysis and data analysis was performed through SPSS version 20. Results: 63% of the staff of educational institutions perceived discrimination in the health system reform plan at an intermediate level. The staff of the Education Department with mean score of 59.0 had the most and Vali -ye-Asr University employees with mean score of 44.9 had the least perceived discrimination from the implementation of the health system reform program. Among job categories, teachers with mean score of 56.5 had the most and physicians with mean score of 45.1 had the least perceived discrimination from the implementation of the health system reform plan. Increased health costs was associated with increased perceived discrimination while higher socioeconomic status and care services was associated with decreased perceived discrimination. Conclusion: The results of this study showed that the implementation of the health system reform plan has led to different levels of discriminatory perception among staff of educational institutions

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