Data has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.

In this paper, we present an ongoing work on using a household electricity consumption behavior model for recognizing changes in sleep patterns. The work is inspired by recent studies in neuroscience revealing an association between dementia and sleep disorders and more particularly, supporting the hypothesis that insomnia may be a predictor for dementia in older adults. Our approach initially creates a clustering model of normal electricity consumption behavior of the household by using historical data. Then we build a new clustering model on a new set of electricity consumption data collected over a predefined time period and compare the existing model with the built new electricity consumption behavior model. If a discrepancy between the two clustering models is discovered a further analysis of the current electricity consumption behavior is conducted in order to investigate whether this discrepancy is associated with alterations in the resident’s sleep patterns. The approach is studied and initially evaluated on electricity consumption data collected from a single randomly selected anonymous household. The obtained results show that our approach is robust to mining changes in the resident daily routines by monitoring and analyzing their electricity consumption behavior model.

In this study we apply clustering techniques for analyzing and understanding households’ electricity consumption data. The knowledge extracted by this analysis is used to create a model of normal electricity consumption behavior for each particular household. Initially, the household’s electricity consumption data are partitioned into a number of clusters with similar daily electricity consumption profiles. The centroids of the generated clusters can be considered as representative signatures of a household’s electricity consumption behavior. The proposed approach is evaluated by conducting a number of experiments on electricity consumption data of ten selected households. The obtained results show that the proposed approach is suitable for data organizing and understanding, and can be applied for modeling electricity consumption behavior on a household level. © Springer Nature Switzerland AG 2019.

We propose a cluster analysis approach for organizing, visualizing and understanding households’ electricity consumption data. We initially partition the consumption data into a number of clusters with similar daily electricity consumption profiles. The centroids of each cluster can be seen as representative signatures of a household’s electricity consumption behaviors. We evaluate the proposed approach by conducting a number of experiments on electricity consumption data of ten selected households. Our results show that the approach is suitable for data analysis, understanding and creating electricity consumption behavior models.

As average life expectancy continuously rises, assisting the elderly population with living independently is of great importance. Detecting abnormal behaviour of the elderly living at home is one way to assist the eldercare systems with the increase of the elderly population. In this study, we perform an initial investigation to identify abnormal behaviour of household residents using energy consumption data. We conduct an experiment in two parts, the first to identify a suitable prediction algorithm to model energy consumption behaviour, and the second to detect abnormal behaviour. This approach allows for an initial step for the elderly care that has a low cost, is easily deployable, and is non-intrusive.

Objectives To explore the value of simulation modelling in evaluating the effects of strategies to plan and schedule operating room (OR) resources aimed at reducing time to surgery for non-elective orthopaedic inpatients at a Swedish hospital. Methods We applied discrete-event simulation modelling. The model was populated with real world data from a university hospital with a strong focus on reducing waiting time to surgery for patients with hip fracture. The system modelled concerned two patient groups that share the same OR resources: hip-fracture and other non-elective orthopaedic patients in need of surgical treatment. We simulated three scenarios based on the literature and interaction with staff and managers: (1) baseline; (2) reduced turnover time between surgeries by 20â €..min and (3) one extra OR during the day, Monday to Friday. The outcome variables were waiting time to surgery and the percentage of patients who waited longer than 24â €..hours for surgery. Results The mean waiting time in hours was significantly reduced from 16.2â €..hours in scenario 1 (baseline) to 13.3â €..hours in scenario 2 and 13.6â €..hours in scenario 3 for hip-fracture surgery and from 26.0â €..hours in baseline to 18.9â €..hours in scenario 2 and 18.5â €..hours in scenario 3 for other non-elective patients. The percentage of patients who were treated within 24â €..hours significantly increased from 86.4% (baseline) to 96.1% (scenario 2) and 95.1% (scenario 3) for hip-fracture patients and from 60.2% (baseline) to 79.8% (scenario 2) and 79.8% (scenario 3) for patients with other non-elective patients. Conclusions Healthcare managers who strive to improve the timelines of non-elective orthopaedic surgeries may benefit from using simulation modelling to analyse different strategies to support their decisions. In this specific case, the simulation results showed that the reduction of surgery turnover times could yield the same results as an extra OR. © 2017 Published by the BMJ Publishing Group Limited.

This study aims to propose a decision support system based on optimization modelling for operating room resource planning and sequence dependent scheduling of surgery operations. We conduct a simulation experiment using real world data collected from the local hospital to evaluate the proposed model. The obtained results are compared with real surgery schedules, planned at the local hospital. The experiment shows that the efficiency of schedules produced by the proposed model are significantly improved, in terms of less surgery turnover time, increased utilization of operating rooms and minimized make-span, compared to the real schedules. Moreover, the proposed optimization based decision support system enables analysis of surgery scheduling in relation to resource planning.

The article presents a new algorithm for handling concept drift: the Trigger-based Ensemble (TBE) is designed to handle concept drift in surgery prediction but it is shown to perform well for other classification problems as well. At the primary care, queries about the need for surgical treatment are referred to a surgeon specialist. At the secondary care, referrals are reviewed by a team of specialists. The possible outcomes of this review are that the referral: (i) is canceled, (ii) needs to be complemented, or (iii) is predicted to lead to surgery. In the third case, the referred patient is scheduled for an appointment with a surgeon specialist. This article focuses on the binary prediction of case three (surgery prediction). The guidelines for the referral and the review of the referral are changed due to, e.g., scientific developments and clinical practices. Existing decision support is based on the expert systems approach, which usually requires manual updates when changes in clinical practice occur. In order to automatically revise decision rules, the occurrence of concept drift (CD) must be detected and handled. The existing CD handling techniques are often specialized; it is challenging to develop a more generic technique that performs well regardless of CD type. Experiments are conducted to measure the impact of CD on prediction performance and to reduce CD impact. The experiments evaluate and compare TBE to three existing CD handling methods (AWE, Active Classifier, and Learn++) on one real-world dataset and one artificial dataset. TBA significantly outperforms the other algorithms on both datasets but is less accurate on noisy synthetic variations of the real-world dataset.

Computer-simulated experiments have become a cost effective way for engineers to replace real experiments in the area of product development. However, one single computer-simulated experiment can still take a significant amount of time. Hence, in order to minimize the amount of simulations needed to investigate a certain design space, different approaches within the design of experiments area are used. One of the used approaches is to minimize the time consumption and simulations for design space exploration through response surface modeling. The traditional methods used for this purpose are linear regression, quadratic curve fitting and support vector machines. This paper analyses and compares the performance of four machine learning methods for the regression problem of response surface modeling. The four methods are linear regression, support vector machines, M5P and random forests. Experiments are conducted to compare the performance of tree models (M5P and random forests) with the performance of non-tree models (support vector machines and linear regression) on data that is typical for concept evaluation within the aerospace industry. The main finding is that comprehensible models (the tree models) perform at least as well as or better than traditional black-box models (the non-tree models). The first observation of this study is that engineers understand the functional behavior, and the relationship between inputs and outputs, for the concept selection tasks by using comprehensible models. The second observation is that engineers can also increase their knowledge about design concepts, and they can reduce the time for planning and conducting future experiments.