The Algorithm of Deviation Measure for Cluster Models Based on the FOCUS Framework and BIRCH

Feng, Xingjie; Pan, Qianqian

doi:10.1109/iita.2008.427

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…This procedure saves a lot of computation. For Birch clustering [36], we first convert the input of N data points into a tree structure called a "clustering feature (CF)". It is defined as the set of triplets = (N, →…”

Section: Birch Clusteringmentioning

confidence: 99%

Safest Route Detection via Danger Index Calculation and K-Means Clustering

Puthige¹,

Bansal²,

Bindra³

et al. 2021

Computers, Materials &Amp; Continua

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The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations. Using the New York City dataset, which provides us with location tagged crime statistics; we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one. The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results. Moreover, a comparative analysis has been performed among various clustering techniques to obtain best results. we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide safe route to users. The successful implementation would hopefully aid us to curb the ever-increasing crime rates; as it aims to provide the user with a beforehand knowledge of the route they are about to take. A warning that the path is marked high on danger index would convey the basic hint for the user to decide which path to prefer. Thus, addressing a social problem which needs to be eradicated from our modern era.

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Section: Birch Clusteringmentioning

confidence: 99%

Safest Route Detection via Danger Index Calculation and K-Means Clustering

Puthige¹,

Bansal²,

Bindra³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Analysis And Detection of Diabetes Using Data Mining Techniques – Efficiency Comparison

Ramadevi¹,

Yeruva²,

Sravanthi³

et al. 2021

IJSRST

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In a digitized world, data is growing exponentially and it is difficult to analyze the data and give the results. Data mining techniques play an important role in healthcare sector - BigData. By making use of Data mining algorithms it is possible to analyze, detect and predict the presence of disease which helps doctors to detect the disease early and in decision making. The objective of data mining techniques used is to design an automated tool that notifies the patient’s treatment history disease and medical data to doctors. Data mining techniques are very much useful in analyzing medical data to achieve meaningful and practical patterns. This project works on diabetes medical data, classification and clustering algorithms like (OPTICS, NAIVEBAYES, and BRICH) are implemented and the efficiency of the same is examined.

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The Algorithm of Deviation Measure for Cluster Models Based on the FOCUS Framework and BIRCH

Cited by 2 publications

References 4 publications

Safest Route Detection via Danger Index Calculation and K-Means Clustering

Safest Route Detection via Danger Index Calculation and K-Means Clustering

Analysis And Detection of Diabetes Using Data Mining Techniques – Efficiency Comparison

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