Book ChapterDOI
Dengue Prediction Using Hierarchical Clustering Methods
S. Vandhana,J. Anuradha +1 more
- pp 157-168
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TLDR
The results have shown that the frequency of dengue happening or the intensity is considerably reduced in many states and K-Nearest Neighbour algorithm is used for predicting the cluster data for 2018.Abstract:
The occurrence of dengue is rapidly increasing in every year. Considering the welfare of the public, it is essential to have detailed study on the affected areas of dengue and its intensity for the control of disease. This paper uses hierarchical clustering technique to classify the data of dengue cases reported and deaths occurred in various states of India. An agglomerative clustering of ward method is used for clustering. The outcomes are represented in Indian map using shape file with RStudio. The data is predicted for 2018, by logarithmic transformation using linear models of regression. K-Nearest Neighbour algorithm is used for predicting the cluster data for 2018. The results have shown that the frequency of dengue happening or the intensity is considerably reduced in many states.read more
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Journal ArticleDOI
The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review
Rayner Alfred,Joe Henry Obit +1 more
TL;DR: In this article, the authors provide a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works.
Journal ArticleDOI
A Clustering Approach for Mapping Dengue Contingency Plan
Farida Amila Husna,Diana Purwitasari,Bayu Adjie Sidharta,Drigo Alexander Sihombing,Amiq Fahmi,Mauridhi Hery Purnomo +5 more
TL;DR: In this paper , a cluster-based dengue contingency planning method was introduced by grouping patient cases according to their environment and demographics, then mapping out a plan and selecting the appropriate plan for each area.
References
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Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Book
Finding Groups in Data: An Introduction to Cluster Analysis
TL;DR: An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.
BookDOI
Finding Groups in Data
TL;DR: In this article, an electrical signal transmission system for railway locomotives and rolling stock is proposed, where a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count, and a spike pulse of greater selected amplitude is transmitted, occurring immediately after the axle count pulse to which it relates, whenever an overheated axle box is detected.
Journal ArticleDOI
Data mining: practical machine learning tools and techniques with Java implementations
Ian H. Witten,Eibe Frank +1 more
TL;DR: This presentation discusses the design and implementation of machine learning algorithms in Java, as well as some of the techniques used to develop and implement these algorithms.