DBSCAN: Past, present and future

Rehman, Saif Ur; Asghar, Sohail; Fong, Simon; Sarasvady, S.

doi:10.1109/icadiwt.2014.6814687

Cited by 283 publications

(105 citation statements)

References 17 publications

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“…On the basis of these definitions, the goal of DBSCAN is to find some points which satisfy the minimum density requirement of MinPts points within the eps distance to be mareked as cores. Then, expand the cores using transitive similarity to include border points, as illustrated in Algorithm 1 (Rehman et al 2014). In our work, constructing the value space of an attribute (see Algorithm 1: Pseudo-code of the DBSCAN as discussed in Rehman et al (2014) Input : @dataset, @eps, @minPts Output: Clusteres of Dataset 1 Function DBSCAN(dataset, eps, minPts ) continue "Constructing the value space of an attribute" subsection) is modeled as an application of DBSCAN.…”

Section: Dbscan Clustering Algorithmmentioning

confidence: 99%

Automated extraction of attributes from natural language attribute-based access control (ABAC) Policies

2019

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The National Institute of Standards and Technology (NIST) has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy (NLACP) to a machine-readable form. To study the automation process, we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations. Therefore, this paper focuses on the questions of: how can we automatically infer the hierarchical structure of an ABAC model given NLACPs; and, how can we extract and define the set of authorization attributes based on the resulting structure. To address these questions, we propose an approach built upon recent advancements in natural language processing and machine learning techniques. For such a solution, the lack of appropriate data often poses a bottleneck. Therefore, we decouple the primary contributions of this work into: (1) developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts, and (2) generating a set of realistic synthetic natural language access control policies (NLACPs) to evaluate the proposed framework. Our experimental results are promising as we achieved -in average -an F1-score of 0.96 when extracting attributes values of subjects, and 0.91 when extracting the values of objects' attributes from natural language access control policies.

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Section: Dbscan Clustering Algorithmmentioning

confidence: 99%

Automated extraction of attributes from natural language attribute-based access control (ABAC) Policies

2019

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“…epsilon and minimum number of points. Parameter epsilon is defined as the radius of neighborhood around a certain point x, and parameter minimum number of points as the minimum quantity of neighbors in the limit of the radius [49]. Minimum number of points was set to 5, and optimal epsilon value was determined based on kNN plot which is equal 2.0 ( Fig.…”

Section: Molecular Docking Of Hsa Withmentioning

confidence: 99%

In vitro and in silico identification of the mechanism of interaction of antimalarial drug – artemisinin with human serum albumin and genomic DNA

Ginosyan

Grabski

Tiratsuyan

2019

Preprint

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Highlights• Artemisinin quenches the fluorescence of HSA by a static mechanism.• Artemisinin quenches fluorescence of tryptophan.• The optimized HSA structure was obtained through molecular dynamics simulations.• Artemisinin binds with HSA in Drug site I and forms a hydrogen bond with Arg218.• Dexamethasone binds with HSA in Drug site I and forms hydrogen bonds with Arg218, Arg222 and Val343.• A hypothetical scheme of the mechanism of action of artemisinin was proposed. AbstractArtemisinins are secondary metabolites of the medicinal plant Artemisia annua, which has been traditionally used in Chinese medicine. Artemisinins have anti-inflammatory, anticarcinogenic, immunomodulatory, antimicrobial, anthelmintic, antiviral, antioxidant, and other properties. Our preliminary reverse virtual screening demonstrated that the ligand-binding domain of the human glucocorticoid receptor (LBD of hGR) is the optimal target for artemisinin. At the same time, the binding sites for artemisinin with the ligand-binding domain of the human glucocorticoid receptor coincide with those of dexamethasone. However, the pharmacokinetics, pharmacodynamics, and exact molecular targets and mechanisms of action of artemisinin are not well known. In this work, the interaction of artemisinin with human serum albumin (HSA) was studied both in vitro and in silico. The results indicate that artemisinin leads to a decrease in optical absorption and quenching of fluorescence by a static mechanism, which is similar to the effect of dexamethasone. Artemisinin interacts with Drug site I on HSA and forms a hydrogen bond with arginine 218. Retardation of the genomic DNA of sarcoma S-180 cells show that artemisinin does not interact directly with DNA. On the basis of the obtained data, we proposed a hypothetical scheme of the mechanisms of action of artemisinin.

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“…After studying various clustering techniques [5,7,19,33], we chose Formal Concept Analysis (FCA) [16,42] as the backbone for the event detection process of our approach. FCA is incremental and multi-modal (criteria 8 and 6).…”

Section: Fca Preliminaries and Definitionsmentioning

confidence: 99%

“…Objects in the low density zones are considered noise while others in high density regions belong to the group limited by the region. For example, DB-Scan [33] produces a partitional clustering based on density measures. This method studies the neighborhood of each point, and partitions data into dense regions separated by not-so-dense regions.…”

Section: Clustering Techniquesmentioning

confidence: 99%

eVM: An Event Virtual Machine Framework

Mansour

Chbeir

Arnould

2018

Transactions on Large-Scale Data- And Knowledge-Centered Systems XXXIX

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Information and communication technology (ICT) is impacting our daily lives more than ever before. Many existing applications guide users in their daily activities (e.g., navigation through traffic, health monitoring, managing home comfort, socializing with others). Although these applications are different in terms of purpose and application domain, they all detect events and propose actions and decision making aid to users. However, there is no usage of a common backbone for event detection that can be instantiated, re-used, and reconfigured in different use cases. In this paper, we propose eVM, a generic event Virtual Machine able to detect events in different contexts while allowing domain experts to model and define the targeted events prior to detection. eVM simultaneously considers the various features of the defined events (e.g., temporal, geographical), and uses the latter to detect different featurecentric events (e.g., time-centric, location-centric). eVM is based on different components (an event query language, a query compiler, an event detection core, etc.), but mainly the event detection modules are detailed here. We show that eVM is re-usable in different contexts and that the performance of our prototype is quasi-linear in most cases. Our experimental results showed that the detection accuracy is improved when, besides spatio-temporal information, other features are considered.

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DBSCAN: Past, present and future

Cited by 283 publications

References 17 publications

Automated extraction of attributes from natural language attribute-based access control (ABAC) Policies

Automated extraction of attributes from natural language attribute-based access control (ABAC) Policies

In vitro and in silico identification of the mechanism of interaction of antimalarial drug – artemisinin with human serum albumin and genomic DNA

eVM: An Event Virtual Machine Framework

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