An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia

Sammour, Mahmoud; Othman, Zulaiha Ali

doi:10.18517/ijaseit.6.6.1482

Cited by 16 publications

(12 citation statements)

References 14 publications

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“…However, several researchers attempted to modify DTW for instance, a modified DTW in terms of time-consuming [14], and make DTW suitable for specific domain, specifically Gene expressions [11]. Furthermore, a modified DTW is proposed to be able to identify similarity via global averaging mechanism rather than the pair-wise sequence matching [10].…”

Section: Resultsmentioning

confidence: 99%

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Modified Dynamic Time Warping for Hierarchical Clustering

Sammour

Othman

Rus

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Time series clustering is the process of grouping sequential correspondences in similar clusters. The key feature behind clustering time series data lies on the similarity/distance function used to identify the sequential matches. Dynamic Time Warping (DTW) is one of the common distance measures that have demonstrated competitive results compared to other functions. DTW aims to find the shortest path in the process of identifying sequential matches. DTW relies on dynamic programming to obtain the shortest path where the smaller distance is being computed. However, in the case of equivalent distances, DTW is selecting the path randomly. Hence, the selection could be misguided in such randomization process, which significantly affects the matching quality. This is due to randomization may lead to the longer path which drifts from obtaining the optimum path. This paper proposes a modified DTW that aims to enhance the dynamic selection of the shortest path when handling equivalent distances. Experiments were conducted using twenty UCR benchmark datasets. Also, the proposed modified DTW result has been compared with the state of the art competitive distance measures which is based on precision, recall and f-measure including the original DTW, Minkowski distance measure and Euclidean distance measure. The results showed that the proposed modified DTW reveal superior results in compared to the standard DTW, either using Minkowski or Euclidean. This can demonstrate the effectiveness of the proposed modification in which optimizing the shortest path has enhanced the performance of clustering. The proposed modified DTW can be used for having good clustering method for any time series data.

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Section: Resultsmentioning

confidence: 99%

“…The problem of time-consumption is when using DTW for large-scale time series clustering [14], [29]. A novel approximation for DTW in which the DTW distances can be bound between LB-Keogh (LB) and Euclidian Distance (ED) functions.…”

Section: Methodsmentioning

confidence: 99%

Modified Dynamic Time Warping for Hierarchical Clustering

Sammour

Othman

Rus

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Entropy is used with various pre-processing methods such as a wrapper, filter for feature elimination, reduction and selection. [20,21]…”

Section: Evaluation Of Clustermentioning

confidence: 99%

A Hotel Recommender System using Context-Based Clustering

Bafna¹,

Pramod²

2019

IJRTE

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The web is one of the largest textual data repositories in the world. There is voluminous data in the digital world. To search for online hotels based on specific requirements of the user is not a very easy job. Ratings and reviews available on different travel websites help to some extent but gives generalized recommendations. A recommender system (RS) which uses reviews is known as content-based and is preferred, to produce a recommendation. Proposed RS maps all requirements of a traveler to features of a hotel and produces person specific recommendation. Phrase-based Recommender System is proposed to reduce efforts and time as compared with a traditional generalized recommender system. The proposed approach makes use of hotel reviews downloaded from TripAdvisor site. The technique initiates with phrase-based feature extraction followed by iterative clustering and ends with feature mapping and exports more relevant recommendations. Betterment of a technique is proved in terms of relevance, accuracy, scalability, and consistency by comparing precision and entropy refinement and corpus size with existing technique.

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“…A considerable number of techniques have been proposed in the literature for clustering, amongst which the already mentioned K-means [26] and hierarchical clustering [27,28] are probably the most widely used. One characteristic of K-means is that this algorithm is simple and easy to understand.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Machine Learning for Advanced Tolerance Monitoring of Wire Electrical Discharge Machining of Disc Turbine Fir-Tree Slots

Wang

Sánchez

Ayesta

et al. 2018

Sensors

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Manufacturing more efficient low pressure turbines has become a topic of primary importance for aerospace companies. Specifically, wire electrical discharge machining of disc turbine fir-tree slots has attracted increasing interest in recent years. However, important issues must be still addressed for optimum application of the WEDM process for fir-tree slot production. The current work presents a novel approach for tolerance monitoring based on unsupervised machine learning methods using distribution of ionization time as a variable. The need for time-consuming experiments to set-up threshold values of the monitoring signal is avoided by using K-means and hierarchical clustering. The developments have been tested in the WEDM of a generic fir-tree slot under industrial conditions. Results show that 100% of the zones classified into Clusters 1 and 2 are related to short-circuit situations. Further, 100% of the zones classified in Clusters 3 and 5 lie within the tolerance band of ±15 μm. Finally, the 9 regions classified in Cluster 4 correspond to situations in which the wire is moving too far away from the part surface. These results are strongly in accord with tolerance distribution as measured by a coordinate measuring machine.

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An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia

Cited by 16 publications

References 14 publications

Modified Dynamic Time Warping for Hierarchical Clustering

Modified Dynamic Time Warping for Hierarchical Clustering

A Hotel Recommender System using Context-Based Clustering

Unsupervised Machine Learning for Advanced Tolerance Monitoring of Wire Electrical Discharge Machining of Disc Turbine Fir-Tree Slots

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