Using internal evaluation measures to validate the quality of diverse stream clustering algorithms

Hassani, Marwan; Seidl, Thomas

doi:10.1007/s40595-016-0086-9

Cited by 77 publications

(44 citation statements)

References 27 publications

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“…The use of different types and number of features may impact the clustering algorithm, modifying its resulting cluster formation. To assess which CBRA strategy achieves the best internal measure, we calculate the Xie‐Bien (XB) cluster index I XB , which obtains the ratio of cluster compactness by cluster separation, given by

I_{XB} = \frac{\sum_{i} \sum_{boldx \in {boldC}_{i}} d^{2} false(boldx, {boldc}_{i} false)}{n \cdot \min_{i \neq j} d^{2} false({boldc}_{i}, {boldc}_{j} false)} .

…”

Section: Resultsmentioning

confidence: 99%

Clustering‐Based Resource Allocation mechanism in Long Term Evolution Advanced networks with auto‐encoder for feature learning

Santos¹

2019

Trans Emerging Tel Tech

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Clustering‐Based Resource Allocation (CBRA) is a relatively recent approach applied in some communication network technologies that executes resource allocation rules by following patterns discovered in the user and traffic flow information. It is a useful option for classifying traffic and detecting the most suitable Quality‐of‐Service parameters to perform improved resource allocation, especially in mobile systems such as Long Term Evolution Advanced, which are growing in user demand. An open issue in the CBRA approach is the necessity to perform feature engineering manually (feature selection), which may cause loss of relevant system information and consequent deterioration of performance. In this research article, we present a novel CBRA mechanism that implements an auto‐encoder to perform feature learning, thus avoiding feature selection. The auto‐encoder, deployed at the early stages of the CBRA mechanism, transforms the user and traffic flow data into a shortened representation, preventing the curse of dimensionality while capturing the nonlinear correlations of the data. The simulation results indicate better cluster formation and performance improvement for real‐time video applications using the proposed mechanism.

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I_{XB} = \frac{\sum_{i} \sum_{boldx \in {boldC}_{i}} d^{2} false(boldx, {boldc}_{i} false)}{n \cdot \min_{i \neq j} d^{2} false({boldc}_{i}, {boldc}_{j} false)} .

…”

Section: Resultsmentioning

confidence: 99%

Clustering‐Based Resource Allocation mechanism in Long Term Evolution Advanced networks with auto‐encoder for feature learning

Santos¹

2019

Trans Emerging Tel Tech

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“…However, in a stream data environment, it is impractical to use an extrinsic evaluation method because it is nearly impossible to provide the ground truth. Thus, using an intrinsic evaluation method is the only realistic and efficient way to evaluate the clustering quality [20].…”

Section: Resultsmentioning

confidence: 99%

Stream Clustering and Visualization of Geotagged Text Data for Crisis Management

Crossman

Chung

Schmidt

2019

2019 International Conference on Data and Software Engineering (ICoDSE)

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Stream Clustering and Visualization of Geotagged Text Data for Crisis Management. In the last decade, the advent of social media and microblogging services have inevitably changed our world. These services produce vast amounts of streaming data, and one of the most important ways of analyzing and discovering interesting trends in the streaming data is through clustering. In clustering streaming data, it is desirable to perform a single pass over incoming data, such that we do not need to process old data again, and the clustering model should evolve over time not to lose any important feature statistics of the data. In this research, we have developed a new clustering system that clusters social media data based on their textual content and displays the clusters and their locations on the map. Our system takes advantage of a text stream clustering algorithm, which uses the two-phase clustering process. The online micro-clustering phase incrementally creates micro-clusters, called text droplets, that represent enough information about topics occurring in the text stream. The off-line macro-clustering phase clusters micro-clusters for a user-specified time interval and can change macro-clustering algorithms dynamically. Our experiments demonstrated that the performance of our system is scalable; and it can be easily used by first responders and crisis management personnel to quickly determine if a crisis is happening, where it is concentrated, and what resources are best to deploy to the situation. v TABLE OF CONTENTS CHAPTER 1 INTRODUCTION .

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“…The objective of internal validation is to examine the compactness/cohesion and the separation of the clusters [ 43 ]. There are various internal validation measures and they are variations of these two.…”

Section: Methodsmentioning

confidence: 99%

A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration

et al. 2018

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BackgroundDrug repositioning is the process of identifying new uses for existing drugs. Computational drug repositioning methods can reduce the time, costs and risks of drug development by automating the analysis of the relationships in pharmacology networks. Pharmacology networks are large and heterogeneous. Clustering drugs into small groups can simplify large pharmacology networks, these subgroups can also be used as a starting point for repositioning drugs. In this paper, we propose a two-tiered drug-centric unsupervised clustering approach for drug repositioning, integrating heterogeneous drug data profiles: drug-chemical, drug-disease, drug-gene, drug-protein and drug-side effect relationships.ResultsThe proposed drug repositioning approach is threefold; (i) clustering drugs based on their homogeneous profiles using the Growing Self Organizing Map (GSOM); (ii) clustering drugs based on drug-drug relation matrices based on the previous step, considering three state-of-the-art graph clustering methods; and (iii) inferring drug repositioning candidates and assigning a confidence value for each identified candidate. In this paper, we compare our two-tiered clustering approach against two existing heterogeneous data integration approaches with reference to the Anatomical Therapeutic Chemical (ATC) classification, using GSOM. Our approach yields Normalized Mutual Information (NMI) and Standardized Mutual Information (SMI) of 0.66 and 36.11, respectively, while the two existing methods yield NMI of 0.60 and 0.64 and SMI of 22.26 and 33.59. Moreover, the two existing approaches failed to produce useful cluster separations when using graph clustering algorithms while our approach is able to identify useful clusters for drug repositioning. Furthermore, we provide clinical evidence for four predicted results (Chlorthalidone, Indomethacin, Metformin and Thioridazine) to support that our proposed approach can be reliably used to infer ATC code and drug repositioning.ConclusionThe proposed two-tiered unsupervised clustering approach is suitable for drug clustering and enables heterogeneous data integration. It also enables identifying reliable repositioning drug candidates with reference to ATC therapeutic classification. The repositioning drug candidates identified consistently by multiple clustering algorithms and with high confidence have a higher possibility of being effective repositioning candidates.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2123-4) contains supplementary material, which is available to authorized users.

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Using internal evaluation measures to validate the quality of diverse stream clustering algorithms

Cited by 77 publications

References 27 publications

Clustering‐Based Resource Allocation mechanism in Long Term Evolution Advanced networks with auto‐encoder for feature learning

Clustering‐Based Resource Allocation mechanism in Long Term Evolution Advanced networks with auto‐encoder for feature learning

Stream Clustering and Visualization of Geotagged Text Data for Crisis Management

A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration

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