Outliers detection method using clustering in buildings data

Habib, Usman; Zucker, Gerhard; Blöchle, Max; Judex, Florian; Haase, Jan

doi:10.1109/iecon.2015.7392181

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…Habib et al [239] discussed the steps involved for detecting outliers in the data obtained from absorption chiller using their on/off state information. The authors also proposed a method for automatic detection of the on/off and/or missing data status of the chiller.…”

Section: Sensor Data Analysis/miningmentioning

confidence: 99%

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

Bae

Cui

et al. 2020

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Section: Sensor Data Analysis/miningmentioning

confidence: 99%

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

Bae

Cui

et al. 2020

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“…There are various methods available for outlier detection in data (Habib, Hayat, & Zucker, 2016;Habib, Zucker, Blochle, Judex, & Haase, 2015;Zucker, Habib, Blochle, Judex, & Leber, 2015). The combination of both Outlier Detection Algorithm (ODA) and J48 classifier gives quite accurate results (Devi & Devi, 2016).…”

Section: Malignant Cells Detection and Classificationsmentioning

confidence: 99%

Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images

Saba

Khan

Islam

et al. 2019

Microscopy Res & Technique

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The advancement of computer‐ and internet‐based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient–doctor diagnostics is extended to a more robust advanced concept of E‐health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud‐based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape‐based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross‐validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.

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“…The [20] used the X-Means clustering algorithm for automatically detecting the system states (ON/OFF), to examine the operational data of adsorption. The ON/OFF state information can also be used for finding outliers in the data [21]- [23].…”

Section: State Of the Artmentioning

confidence: 99%

Finding the Different Patterns in Buildings Data Using Bag of Words Representation with Clustering

Habib

Zucker

2015

2015 13th International Conference on Frontiers of Information Technology (FIT)

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The understanding of the buildings operation has become a challenging task due to the large amount of data recorded in energy efficient buildings. Still, today the experts use visual tools for analyzing the data. In order to make the task realistic, a method has been proposed in this paper to automatically detect the different patterns in buildings. The K-Means clustering is used to automatically identify the ON (operational) cycles of the chiller. In the next step the ON cycles are transformed to symbolic representation by using Symbolic Aggregate Approximation (SAX) method. Then the SAX symbols are converted to bag of words representation for hierarchical clustering. Moreover, the proposed technique is applied to real life data of adsorption chiller. Additionally, the results from the proposed method and dynamic time warping (DTW) approach are also discussed and compared.

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Outliers detection method using clustering in buildings data

Cited by 14 publications

References 18 publications

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

Literature Review for Sensor Impact Evaluation and Verification Use Cases - Building Controls and Fault Detection and Diagnosis (FDD)

Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images

Finding the Different Patterns in Buildings Data Using Bag of Words Representation with Clustering

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