Predicting Erroneous Financial Statements Using a Density-Based Clustering Approach

2020 ◽  
Author(s):  
Martha Tatusch ◽  
Gerhard Klassen ◽  
Marcus Bravidor ◽  
Stefan Conrad
Keyword(s):  
Financial Statements ◽  
Density Based Clustering ◽  
Clustering Approach

scholarly journals Proposing a Density-Based Clustering Approach (DBCA) to Aggregate Data Collected from the Environment in Arid Area for Desertification

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhihao Peng ◽  
Raziyeh Daraei ◽  
Seyed Mojtaba Ahmadpanahi ◽  
Amir Seyed Danesh ◽  
Safieh Siadat ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Arid Area ◽  
Sensor Nodes ◽  
Base Stations ◽  
Wireless Sensor ◽  
Arid Areas ◽  
Density Based Clustering ◽  
Energy Limitation ◽  
Clustering Approach

Nowadays, the expansion of desert areas has become one of the main problems in arid areas due to various reasons such as rising temperatures and vegetation fires. Establishment of wireless sensor networks in these areas can accelerate the process of environmental monitoring and integrate temperature and humidity information sending to base stations in order to make basic decisions on desertification. The main problem in this regard is the energy limitation of sensor nodes in wireless sensor networks, which is one of the main challenges in using these nodes due to the lack of a fixed power supply. Because the node consumes the most energy during data transmission, the node that transmits the most data or sends the packets over long distances runs out of energy faster than the others and the network work process is disrupted. Therefore, in this study, a density-based clustering approach is proposed to integrate data collected from the environment in arid areas for desertification. In the proposed method at each step, the node that has the most residual energy and is highly centralized will be selected to transfer information. The results of experiments for evaluating the performance of the proposed method show that the proposed method balances the energy consumption of the nodes and optimizes the lifespan of the nodes in the wireless sensor network installed in the arid area.

scholarly journals Phase and interface determination in computer simulations of liquid mixtures with high partial miscibility

2017 ◽  
Vol 19 (29) ◽  
pp. 18968-18974 ◽  
Author(s):  
Marcello Sega ◽  
György Hantal
Keyword(s):  
Computer Simulation ◽  
Computer Simulations ◽  
Liquid Mixtures ◽  
Mutual Solubility ◽  
Linear Scaling ◽  
Phase Identification ◽  
Partial Miscibility ◽  
Density Based Clustering ◽  
Partially Miscible ◽  
Clustering Approach

Partially miscible solutions can represent a challenge from the computer simulation standpoint, especially if the mutual solubility of the components is so large that their concentrations do not change much from one phase to another. A density-based clustering approach with quasi-linear scaling is shown to provide consistent phase identification.

A novel approach for Density based Optimal Semantic Clustering of Web Objects via identification of KingPins

2020 ◽  
Vol 13 ◽  
Author(s):  
Sonia Setia ◽  
Jyoti Verma ◽  
Neelam Duhan
Keyword(s):  
Computational Complexity ◽  
Similarity Measure ◽  
Prediction Models ◽  
Semantic Similarity Measure ◽  
Semantic Clustering ◽  
Clustering Technique ◽  
Clustering Model ◽  
Web Objects ◽  
Density Based Clustering ◽  
Clustering Approach

Background: Clustering is one of the important techniques in Data Mining to group the related data. Clustering can be applied on numerical data as well as web objects such as URLs, websites, documents, keywords etc. which is the building block for many recommender systems as well as prediction models. Objective: The objective of this research article is to develop an optimal clustering approach which considers semantics of web objects to cluster them in a group. More so importantly, the purpose of the proposed work is to strictly improve the computation time of clustering process. Methods: In order to achieve the desired objectives, following two contributions have been proposed to improve the clustering approach 1) Semantic Similarity Measure based on Wu-Palmer Semantics based similarity 2). Two-Level Densitybased Clustering technique to reduce the computational complexity of density based clustering approach. Results: The efficacy of the proposed method has been analyzed on AOL search logs containing 20 million web queries. The results showed that our approach increases the F-measure, and decreases the entropy. It also reduces the computational complexity and provides a competitive alternative strategy of semantic clustering when conventional methods do not provide helpful suggestions. Conclusion: A clustering model has been proposed which is composed of two components i.e. Similarity measure and Density based two-level clustering technique. The proposed model reduced the time cost of density based clustering approach without effecting the performance.

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