hStreams is a recently proposed (IPDPSW 2016) task-based target-agnostic heterogeneous streaming library that supports task concurrency over heterogeneous platforms. We share our experience of enabling a non-trivial machine learning (ML) algorithm: K-nearest neighbor using hStreams. The K-nearest neighbor (KNN) is a popular algorithm with numerous applications in machine learning, data-mining, ...

The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pas...

Abstract The target (dependent) variable is often influenced not only by ratio scale variables, but also qualitative (nominal scale) variables in classification analysis. Majority of machine learning techniques accept numerical inputs. Hence, it necessary to encode these categorical into values using encoding techniques. If the does have relation or order between its values, assigning numbers w...

Continuous monitoring of k nearest neighbor (kNN) queries has attracted significant research attention in the past few years. A safe region is an area such that as long as a kNN query remains in it, the set of its k nearest neighbors does not change. Hence, the server does not need to update the query results unless the query moves out of its safe region. Previous work uses time-parameterized k...

Density-based clustering algorithms for multivariate data often have difficulties with high-dimensional data and clusters of very different densities.A new density-based clustering algorithm, called KNNCLUST, is presented in this paper that is able to tackle these situations. It is based on the combination of nonparametric k-nearest-neighbor (KNN) and kernel (KNN-kernel) density estimation. The...

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Data mining has been flourishing in the information-based world. In data mining, the DTW-kNN framework is widely applied for classification in miscellaneous application domains. Most of the studies in the DTW-kNN framework focus on accuracy and speedup. However, with increasingly emphasis on applications of mobile and embedded systems, energy efficiency becomes an urgent consideration in data m...

Associating documents to relevant categories is critical for effective document retrieval. Here, we compare the well-known k-Nearest Neighborhood (kNN) algorithm, the centroid-based classifier and the Highest Average Similarity over Retrieved Documents (HASRD) algorithm, for effective document categorization. We use various measures such as the micro and macro F1 values to evaluate their perfor...