Book ChapterDOI
An Enhanced K-Means MSOINN Based Clustering Over Neo4j with an Application to Weather Analysis
K. Lavanya,Rani Kashyap,S. Anjana,Sumaiya Thasneen +3 more
- pp 451-461
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TLDR
In this paper, the authors used Neo4j graph database to represent the area-wise weather report in a simplified, graphical way and make it convenient for the weather analysis, and clustering the dataset by the k-means MSOINN based on self-organizing incremental neural network (SOINN) algorithm.Abstract:
Drastic climate change is one of the environmental concerns faced by all the living beings on earth today. So an efficient weather clustering should be done in order to better consolidate the climatic variations because the climatic predictions are environmental specific. Neo4j, one of the popular NoSQL databases, is used to represent the weather dataset in a graphical manner. The flexibility, scalability, and simplicity of the neo4j graph database help to represent the area-wise weather report in a simplified, graphical way and makes it convenient for the weather analysis. As Neo4j can handle complex and connected data, clustering the dataset by the k-means MSOINN based on Self-Organizing Incremental Neural Network (SOINN) algorithm, which uses the squared root of numerical, and categorical data enhances the accuracy and distribution contrast to the traditional k-means methodology.read more
Citations
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Journal ArticleDOI
Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis
TL;DR: The results show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers.
Journal ArticleDOI
All-in-one: Toward hybrid data collection and energy saving mechanism in sensing-based IoT applications
Marwa Ibrahim,Marwa Ibrahim,Hassan Harb,Hassan Harb,Ali Mansour,Abbass Nasser,Abbass Nasser,Christophe Osswald +7 more
TL;DR: In this article, the authors proposed a hybrid data collection and energy saving mechanism, called All-in-One, for sensing-based IoT applications, which takes advantages from existing data reduction techniques while optimizing various performance metrics.
References
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Proceedings ArticleDOI
Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm
Shi Na,Liu Xu-min,Guan Yong +2 more
TL;DR: An improved k-means algorithm is proposed, requiring a simple data structure to store some information in every iteration, which is to be used in the next interation, saving the running time and reducing the computational complexity of the k-mine.
Proceedings ArticleDOI
Research on architecture and query performance based on distributed graph database Neo4j
Hongcheng Huang,Ziyu Dong +1 more
TL;DR: This paper analyzes the experimental results and selection suggestions of query ways are recommended later and provides a reference of query performance optimization for specific business applications.
Proceedings ArticleDOI
Graph databases: A survey
TL;DR: An overview of the different type of graph databases, applications, and comparison between their models based on some properties is given.
Journal ArticleDOI
An incremental mixed data clustering method using a new distance measure
TL;DR: This paper introduces a mixed data clustering method which is incremental and generates a sufficient number of clusters automatically and is compared with the ASOINN and three other clustering algorithms comprehensively.
Proceedings ArticleDOI
Tensor fusion of social structural and functional analytics over Neo4j
TL;DR: A tensor based methodology is proposed for combining functional characteristics with structural metrics, also a structural ranking which can be computed equally efficiently when the graph represents a network.