Data Mining Practical Machine Learning Tools and Techniques

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Handbook of Data Mining and Knowledge Discovery

Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems

The Internet of Things is expected to generate an unprecedented number of unbounded data streams that will produce a paradigm shift when it comes to data analytics. We are moving away from performing analytics in a public or private cloud to performing analytics locally at the fog and edge resources. In this paper, we propose a network of tasks utilizing edge, fog, and cloud computing that are designed to support an Analytics Everywhere framework. The aim is to integrate a variety of computational resources and analytical capabilities according to a data life-cycle. We demonstrate the proposed framework using an application in smart transit.

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Analytics Everywhere: Generating Insights From the Internet of Things

The existing action recognition systems mainly focus on generalized methods to categorize human actions. However, the generalized systems cannot attain the same level of recognition performance for new users mainly due to the high variance in terms of human behavior and the way of performing actions, i.e. activity handling. The use of personalized models based on similarity was introduced to overcome the activity handling problem, but the improvement was found to be limited as the similarity was based on physiognomies rather than the behavior. Moreover, human interaction with contextual information has not been studied extensively in the domain of action recognition. Such interactions can provide an edge for both recognizing high-level activities and improving the personalization effect. In this paper, we propose the context-aware personalized human activity recognition (CAPHAR) framework which computes the class association rules between low-level actions/sensor activations and the contextual information to recognize high-level activities. The personalization in CAPHAR leverages the individual behavior process using a similarity metric to reduce the effect of the activity handling problem. The experimental results on the “daily lifelog” dataset show that CAPHAR can achieve at most 23.73% better accuracy for new users in comparison to the existing classification methods.

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CAPHAR: context-aware personalized human activity recognition using associative learning in smart environments

Computational social sensing is enabled by the Internet of Things at large scale. Using sensors, e. g., implemented in mobile and wearable devices, human behavior and activities can then be investigated, e.g., using according models and patterns. However, the obtained models are often not explicative, i. e., interpretable, transparent, and explanation-aware, which makes assessment and validation difficult for humans. This paper proposes a novel explicative classification approach featuring interpretable and explainable models. For this purpose, we embed a framework for building rule-based classifiers using class association rules. For evaluation, we apply two real-world datasets: One collected in the domain of personalized health using wearable sensors (accelerometers), the second one utilizing smartphone sensors for activity recognition. Our results indicate, that the proposed approach outperforms the baselines clearly, concerning both accuracy and complexity of the resulting predictive models.

Explicative human activity recognition using adaptive association rule-based classification

For supporting interpretation, assessment and application of data mining models, explanation-aware methods are crucial. This paper presents an approach for explanation-aware feature selection and assessment using symbolic abstractions of time series. For that, we utilize the symbolic approximate aggregation (SAX) method for data abstraction to be implemented into data mining models. We investigate several approaches and discuss experiences in the context of petro-chemical production.

Explanation-aware feature selection using symbolic time series abstraction: Approaches and experiences in a petro-chemical production context

Anticipatory systems require different steps like sensing, data processing, context inference, and context prediction. Then, suitable platforms can support the implementation of the respective steps. This paper proposes an anticipatory ubiquitous perspective on the Ubicon platform, considering data capture (sensing), localization (context inference) and activity recognition (context prediction), enabled by an integration of different technologies and tools. In an integrated approach, we propose different components for augmenting the Ubicon platform. For these, we present results of respective case studies in ubiquitous and social environments. Our results demonstrate the applicability of the Ubicon platform for these tasks, towards an extended platform for anticipatory ubiquitous computing.

Sensing, processing and analytics: augmenting the U bicon platform for anticipatory ubiquitous computing

The analysis of human activity data is an important research area in the context of ubiquitous and social environments. Using sensor data obtained by mobile devices, e. g., utilizing accelerometer sensors contained in mobile phones, behavioral patterns and models can then be obtained. However, the utilized models are often not simple to interpret by humans in order to facilitate assessment, evaluation and validation, e. g., in computational social science or in medical contexts. In this paper, we propose a novel approach for generating interpretable rule sets for classification: We present an adaptive framework for mining class association rules using subgroup discovery, and analyze different techniques for obtaining the final classifier. The approach is investigated in the context of human activity recognition. For our evaluation, we apply real-world activity data collected using mobile phone sensors.

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Naveed Hayat

Papers

Explicative human activity recognition using adaptive association rule-based classification

Explanation-aware feature selection using symbolic time series abstraction: Approaches and experiences in a petro-chemical production context

Sensing, processing and analytics: augmenting the U bicon platform for anticipatory ubiquitous computing

Adaptive class association rule mining for human activity recognition