Data Mining with Big Data

Sowmya, R.; Suneetha, K R

doi:10.1109/isco.2017.7855990

Cited by 70 publications

(42 citation statements)

References 15 publications

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“…i } represented by the frequency of each sub-range, which is the proportion of data points that each sub-range covers. Thus, the information entropy of the i-th dimension can be calculated by Equation (1). Note that the data block splitter selects to split data points into blocks in a specific number of dimension, with the criteria that the information entropy in these dimension are the largest ones.…”

Section: A Data Block Splittermentioning

confidence: 99%

“…With the growing of large collection of data in various domains like business management and cloud services [1], much attention has been given to data mining algorithms, which can be applied to many tasks such as event detection [2], personalized recommendation [3] and the Internet of Things (IoT) [4]. As a typical data mining algorithm, clustering shows broad applications in data analysis, where density-based clustering algorithms play a crucial role [5].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Density-Based Spatial Clustering for Massive Data Analysis

Cai

Wang

2020

IEEE Access

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Clustering is a classical research field due to its broad applications in data mining such as emotion detection, event extraction and topic discovery. It aims to discover intrinsic patterns which can be formed as clusters from a collection of data. Significant progress have been made by the Density-based Spatial Clustering of Applications with Noise (DBSCAN) and its variants. However, there is a major limitation that current density-based algorithms suffer from linear connection problem, where they perform poorly to discriminate objective clusters which are ''connected'' by a few data points. Moreover, the parameter setting and the time cost make it hard to be well-adapted in massive data analysis. To address these problems, we propose a novel adaptive density-based spatial clustering algorithm called Ada-DBSCAN, which consists of a data block splitter and a data block merger, coordinated by local clustering and global clustering. We conduct extensive experiments on both artificial and real-world datasets to evaluate the effectiveness of Ada-DBSCAN. Experimental results show that our algorithm evidently outperforms several strong baselines in both clustering accuracy and human evaluation. Besides, Ada-DBSCAN shows significant improvement of efficiency compared with DBSCAN. INDEX TERMS Clustering, density-based algorithms, linear connection, data block splitter, data block merger.

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Section: A Data Block Splittermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Density-Based Spatial Clustering for Massive Data Analysis

Cai

Wang

2020

IEEE Access

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“…The Database Administration Component is linked to all other components and three databases: 'Authorities database' where the profiles of criminals are stored, 'Crowd Database' which contains all the participant identification data and finally, 'Crowdsensed Information DB' which is reserved for storing all data collected by the participants. Given the heterogeneity of the data (structured and unstructured) to be stored, we adopt a document-oriented database management system and more specifically MongoDB [18].…”

Section: The Architecturementioning

confidence: 99%

A mobile crowd sensing framework for suspect investigation: An objectivity analysis and de-identification approach

et al. 2020

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The ubiquity of mobile devices and their advanced features have increased the use of crowdsourcing in many areas, such as the mobility in the smart cities. With the advent of high-quality sensors on smartphones, online communities can easily collect and share information. These information are of great importance for the institutions, which must analyze the facts by facilitating the data collecting on crimes and criminals, for example. This paper proposes an approach to develop a crowdsensing framework allowing a wider collaboration between the citizens and the authorities. In addition, this framework takes advantage of an objectivity analysis to ensure the participants' credibility and the information reliability, as law enforcement is often affected by unreliable and poor quality data. In addition, the proposed framework ensures the protection of users' private data through a de-identification process. Experimental results show that the proposed framework is an interesting tool to improve the quality of crowdsensing information in a government context.

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“…A survey is done to study the impact of data mining and the effect of mining algorithms on healthcare data especially cardiovascular data in the direction of prediction of heart diseases. [10] Emphasizes on the amount of data which is produced in various sectors like meteorology, complex physics simulation, business, healthcare etc. And also states that the data produced is huge and rich and varied.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining Algorithms on Prediction of Cardiovascular Diseases

Singh*¹

2019

IJRTE

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In the age of data generation known as Big Data, where data is produced in enormous amount, managing it has become a big challenge and along with this drawing information from the gathered data is equally important and challenging. Inferring relationships and predicting patterns from theses structured and unstructured data is now an area of research for researchers. And the data mining techniques have evolved as a tool for generating results and deducing conclusions. These mining algorithms find their applicability in almost every domain likewise understanding market segment, fraud detection, trend analysis, healthcare sector, education sector and many more. Looking at the wide range of applicability, in this paper, a brief overview of data mining algorithms is discussed. This discussion comprises of different data mining algorithms, their mathematical modelling, their evaluation methods, and their limitations. To support the fact a case study is conducted on a cardiovascular disease dataset and the measures of these mining techniques are compared.

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Data Mining with Big Data

Cited by 70 publications

References 15 publications

Adaptive Density-Based Spatial Clustering for Massive Data Analysis

Adaptive Density-Based Spatial Clustering for Massive Data Analysis

A mobile crowd sensing framework for suspect investigation: An objectivity analysis and de-identification approach

Data Mining Algorithms on Prediction of Cardiovascular Diseases

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