K- nearest neighbour (KNN)
Sales Forecasting using Linear Regression and K-Nearest Neighbour
... K nearest neighbour is a indispensable calculation that stores every accessible case and groups new cases dependent on a similitude measure ...its neighbour, with the case being assigned to ...
6
Abnormal Moving Object Detection Using Sparse Based Graph K Nearest Neighbour(SGk-NN)
... processing k-nearest neighbour queries is most important in moving object related ...of k-nearest neighbor Queries in centralized setting ...of K Nearest Neighbor Queries ...
6
Personalized Recommendation of Movies Using a Combined approach of locality sensitive hashing, K-Nearest neighbour and collaborative filtering.
... After registering into the system, user’s profiles are analysed and the user profile is generated. Generating the user profile is not focused in this paper. To have a better personalized recommendation, the demographic ...
14
Automated identification of Monogeneans using digital image processing and K nearest neighbour approaches
... train and test the system. K-nearest neighbour was used for detection of four species of monogeneans according to their diagnostic parts. The accuracy of automated identifi- cation in this study is ...
8
Bootstrap inference for K-nearest neighbour matching estimators
... -nearest neighbour (KNN) matching estimators (Abadie and Imbens, 2006) are popular for estimating, non-parametrically, the average causal effect of a binary variable on an outcome in observational studies, ...
28
Heart Disease Prediction System Using K Nearest Neighbour Classification Technique
... The Heart Disease Prediction System using K-Nearest Neighbour Classifier provides its users with a prediction result that gives the state of a user leading to heart problem. The algorithm gives the ...
8
Bootstrap Inference for K-Nearest Neighbour Matching Estimators
... -nearest neighbour (KNN) matching estimators (Abadie and Imbens, 2006) are popular for estimating, non-parametrically, the average causal effect of a binary variable on an outcome in observational studies, ...
25
Improving the Accuracy of K-Nearest Neighbour Method in Long Lead Hydrological Forecasting
... of K-Nearest Neighbour (K-NN) has been used in a variety of eco-hydrological ...the K-NN method in forecasting accumulated 9- month inow, from 1971 to 2001, of Zayandeh-rud dam in Iran, ...
8
A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses
... The recommender system, in this work, was designed to contain a knowledge-base that has accumulated experience and a set of rules for applying the knowledge-base to each particular situation that was described. ...
8
Discretisation of Data in a Binary Neural k-Nearest Neighbour Algorithm
... a k-Nearest Neighbour predictor. Our k-NN is constructed using binary neural networks which require continuous-valued data to be discretised to allow it to be mapped to the binary neural ...
31
Extreme Precipitation Events Simulation Using An Improved K-Nearest Neighbour Weather Generating Model
... upon K- nearest neighbor algorithm do not produce new values but merely reshuffles the historical data to generate realistic weather ...allows nearest neighbour resampling with perturbation of ...
7
Storm Identification in the Rainfall Data Using Singular Value Decomposition and K-Nearest Neighbour Classification
... In this work, designed and implemented a model for relevance feature discovery. It discovers rainfall conditions as higher level features and deploys them over low-level features (terms) using singular value ...
7
Enhancing the Classification Accuracy of Noisy Dataset By Fusing Correlation Based Feature Selection with K-Nearest Neighbour
... the k-NN algorithm is sensitive to irrelevant attributes therefore its performance degrades significantly when a dataset contains missing values or noisy ...the k-NN algorithm can be minimized when combined ...
9
Consensus speed optimisation with finite leadership perturbation in k-nearest neighbour networks
... to k-nearest neighbour (k-NNR) connection rules, by allocating a finite leadership resource amongst selected ...perturbed k-NNR network is identi- fied as the largest element of the ...
6
Scalable Distributed Processing of K nearest Neighbour Queries over Moving Objects
... ABSTRACT: Many applications involving moving objects is the task of processing k-nearest neighbour (k-NN) queries. Most of the existing approaches to this problem are designed for the ...
6
Improving classification performance of k nearest neighbour by hybrid clustering and feature selection for non communicable disease prediction
... than k-nearest neighbour when population covariance matrices are equal, a condition that suggests a linear ...increases, k-nearest neighbour performs increasingly better than the ...
9
Improved AURA k-Nearest Neighbour approach
... orcid.org/0000-0001-5957-2474 et al. (2 more authors) (2003) Improved AURA k-Nearest Neighbour approach. In: Mira, J and Alvarez, JR, (eds.) ARTIFICIAL NEURAL NETS PROBLEM SOLVING METHODS, PT II. 7th ...
10
A binary neural k-nearest neighbour technique
... Abstract. K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering ...data. k-NN is effective but is often criticised for its polynomial run-time growth as ...
19
An Effective Candidate Refinement Approach For High Dimensional Of K-Nearest Neighbour Search
... inside the cache. For each candidate determined within the cache, we compute its lower/higher distance bounds. The next phase focuses on reducing the candidate size (which do no longer incur disk accesses). Among all ...
6
Location And Query Privacy In K Nearest Neighbour Queries
... This article proposes hybrid techniques for approximate and accurate private NN queries that provide protection to users and the service provider. Our solutions rely on cryptographic protocols for the private evaluation ...
9