In this paper we focus on the task of clustering in data mining applications. We introduce a form... more In this paper we focus on the task of clustering in data mining applications. We introduce a formulation of a new clustering algorithm by modelling the system as a cooperative game in strategic form using game theory. The goal is to partition a dataset into k clusters. Our approach has been applied to both simulated and real-world datasets. In addition,
In ubiquitous computing, activity recognition systems can be used to label large sets of data. Re... more In ubiquitous computing, activity recognition systems can be used to label large sets of data. Recognition of activities from data sensors is a key paradigm in ubiquitous computing. Variability in human activities, deployment sensors characteristics, and application domains leads to the elaboration of best practices and methods to improve robustness of activity recognition systems. Classification is an important step who aims to yield the recognition process more expressive and reduce uncertainty, therefore minimize representation. In this report, we address classification methods and showed explicitly how they are used for activity recognition. We propose a method which uses the clustering algorithm k-Medoids. Different stages of construction of the model of classification are showed in detail. Finally validation results are presented.
Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recogn... more Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recognition systems can be used to label large sets of data. Variability in human activities, sensor deployment characteristics, and application domains has led to the development of best practices and methods to improve the robustness of activity recognition systems. Classification is one of the most important steps in making the recognition process more expressive and reducing uncertainty, thus minimizing representation. The K-medoid algorithm is simple but effective for grouping data according to the similarity that the samples present between them without the need to know each sample's membership class. In this paper, we propose a classification technique based on unsupervised partitioning algorithm, which allows recognizing activities and overcomes the problem of supervision.
In ubiquitous computing, activity recognition systems can be used to label large sets of data. Re... more In ubiquitous computing, activity recognition systems can be used to label large sets of data. Recognition of activities from data sensors is a key paradigm in ubiquitous computing. Variability in human activities, deployment sensors characteristics, and application domains leads to the elaboration of best practices and methods to improve robustness of activity recognition systems. Classification is an important step who aims to yield the recognition process more expressive and reduce uncertainty, therefore minimize representation. In this report, we address classification methods and showed explicitly how they are used for activity recognition. We propose a method which uses the clustering algorithm k-Medoids. Different stages of construction of the model of classification are showed in detail. Finally validation results are presented.
Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recogn... more Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recognition systems can be used to label large sets of data. Variability in human activities, sensor deployment characteristics, and application domains has led to the development of best practices and methods to improve the robustness of activity recognition systems. Classification is one of the most important steps in making the recognition process more expressive and reducing uncertainty, thus minimizing representation. The K-medoid algorithm is simple but effective for grouping data according to the similarity that the samples present between them without the need to know each sample's membership class. In this paper, we propose a classification technique based on unsupervised partitioning algorithm, which allows recognizing activities and overcomes the problem of supervision.
International Journal of Ad Hoc and Ubiquitous Computing, 2019
The evaluation of a patient's functional ability to perform daily living activities is an essenti... more The evaluation of a patient's functional ability to perform daily living activities is an essential part of nursing and a powerful predictor of a patient's morbidity, especially for the elderly. In this article, we describe the use of a machine learning approach to address the task of recognising activity in a smart home. We evaluate our approach by comparing it to a Markov statistical approach and using several performance measures over three datasets. We show how our model achieves significantly better recognition performance on certain data sets and with different representations and discretisation methods with an accuracy measurement that exceeds 92% and accuracy of 68%. The experiments also show a significant improvement in the learning time which does not exceed one second in the totality of the experiments reducing the complexity of the approach.
Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, 2011
In this paper we focus on the task of clustering in data mining applications. We introduce a form... more In this paper we focus on the task of clustering in data mining applications. We introduce a formulation of a new clustering algorithm by modelling the system as a cooperative game in strategic form using game theory. The goal is to partition a dataset into k clusters. Our approach has been applied to both simulated and real-world datasets. In addition,
International Journal of Ad Hoc and Ubiquitous Computing, 2019
The evaluation of a patient's functional ability to perform daily living activities is an essenti... more The evaluation of a patient's functional ability to perform daily living activities is an essential part of nursing and a powerful predictor of a patient's morbidity, especially for the elderly. In this article, we describe the use of a machine learning approach to address the task of recognising activity in a smart home. We evaluate our approach by comparing it to a Markov statistical approach and using several performance measures over three datasets. We show how our model achieves significantly better recognition performance on certain data sets and with different representations and discretisation methods with an accuracy measurement that exceeds 92% and accuracy of 68%. The experiments also show a significant improvement in the learning time which does not exceed one second in the totality of the experiments reducing the complexity of the approach.
In this paper we focus on the task of clustering in data mining applications. We introduce a form... more In this paper we focus on the task of clustering in data mining applications. We introduce a formulation of a new clustering algorithm by modelling the system as a cooperative game in strategic form using game theory. The goal is to partition a dataset into k clusters. Our approach has been applied to both simulated and real-world datasets. In addition,
In ubiquitous computing, activity recognition systems can be used to label large sets of data. Re... more In ubiquitous computing, activity recognition systems can be used to label large sets of data. Recognition of activities from data sensors is a key paradigm in ubiquitous computing. Variability in human activities, deployment sensors characteristics, and application domains leads to the elaboration of best practices and methods to improve robustness of activity recognition systems. Classification is an important step who aims to yield the recognition process more expressive and reduce uncertainty, therefore minimize representation. In this report, we address classification methods and showed explicitly how they are used for activity recognition. We propose a method which uses the clustering algorithm k-Medoids. Different stages of construction of the model of classification are showed in detail. Finally validation results are presented.
Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recogn... more Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recognition systems can be used to label large sets of data. Variability in human activities, sensor deployment characteristics, and application domains has led to the development of best practices and methods to improve the robustness of activity recognition systems. Classification is one of the most important steps in making the recognition process more expressive and reducing uncertainty, thus minimizing representation. The K-medoid algorithm is simple but effective for grouping data according to the similarity that the samples present between them without the need to know each sample's membership class. In this paper, we propose a classification technique based on unsupervised partitioning algorithm, which allows recognizing activities and overcomes the problem of supervision.
In ubiquitous computing, activity recognition systems can be used to label large sets of data. Re... more In ubiquitous computing, activity recognition systems can be used to label large sets of data. Recognition of activities from data sensors is a key paradigm in ubiquitous computing. Variability in human activities, deployment sensors characteristics, and application domains leads to the elaboration of best practices and methods to improve robustness of activity recognition systems. Classification is an important step who aims to yield the recognition process more expressive and reduce uncertainty, therefore minimize representation. In this report, we address classification methods and showed explicitly how they are used for activity recognition. We propose a method which uses the clustering algorithm k-Medoids. Different stages of construction of the model of classification are showed in detail. Finally validation results are presented.
Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recogn... more Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recognition systems can be used to label large sets of data. Variability in human activities, sensor deployment characteristics, and application domains has led to the development of best practices and methods to improve the robustness of activity recognition systems. Classification is one of the most important steps in making the recognition process more expressive and reducing uncertainty, thus minimizing representation. The K-medoid algorithm is simple but effective for grouping data according to the similarity that the samples present between them without the need to know each sample's membership class. In this paper, we propose a classification technique based on unsupervised partitioning algorithm, which allows recognizing activities and overcomes the problem of supervision.
International Journal of Ad Hoc and Ubiquitous Computing, 2019
The evaluation of a patient's functional ability to perform daily living activities is an essenti... more The evaluation of a patient's functional ability to perform daily living activities is an essential part of nursing and a powerful predictor of a patient's morbidity, especially for the elderly. In this article, we describe the use of a machine learning approach to address the task of recognising activity in a smart home. We evaluate our approach by comparing it to a Markov statistical approach and using several performance measures over three datasets. We show how our model achieves significantly better recognition performance on certain data sets and with different representations and discretisation methods with an accuracy measurement that exceeds 92% and accuracy of 68%. The experiments also show a significant improvement in the learning time which does not exceed one second in the totality of the experiments reducing the complexity of the approach.
Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, 2011
In this paper we focus on the task of clustering in data mining applications. We introduce a form... more In this paper we focus on the task of clustering in data mining applications. We introduce a formulation of a new clustering algorithm by modelling the system as a cooperative game in strategic form using game theory. The goal is to partition a dataset into k clusters. Our approach has been applied to both simulated and real-world datasets. In addition,
International Journal of Ad Hoc and Ubiquitous Computing, 2019
The evaluation of a patient's functional ability to perform daily living activities is an essenti... more The evaluation of a patient's functional ability to perform daily living activities is an essential part of nursing and a powerful predictor of a patient's morbidity, especially for the elderly. In this article, we describe the use of a machine learning approach to address the task of recognising activity in a smart home. We evaluate our approach by comparing it to a Markov statistical approach and using several performance measures over three datasets. We show how our model achieves significantly better recognition performance on certain data sets and with different representations and discretisation methods with an accuracy measurement that exceeds 92% and accuracy of 68%. The experiments also show a significant improvement in the learning time which does not exceed one second in the totality of the experiments reducing the complexity of the approach.
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