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Cluster Analysis

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Vision-based Pakistani sign language recognition using bag-of-words and support vector machines.

Scientific reports
In order to perform their daily activities, a person is required to communicating with others. This can be a major obstacle for the deaf population of the world, who communicate using sign languages (SL). Pakistani Sign Language (PSL) is used by more...

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm.

Sensors (Basel, Switzerland)
With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor...

N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.

Scientific data
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet difficulties in achieving this ability. Similar challenges also exist for b...

Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms.

PloS one
Next basket recommendation is a critical task in market basket data analysis. It is particularly important in grocery shopping, where grocery lists are an essential part of shopping habits of many customers. In this work, we first present a new groce...

Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data.

Sensors (Basel, Switzerland)
Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes' energy consumption data. From the literature, ...

Examining unsupervised ensemble learning using spectroscopy data of organic compounds.

Journal of computer-aided molecular design
One solution to the challenge of choosing an appropriate clustering algorithm is to combine different clusterings into a single consensus clustering result, known as cluster ensemble (CE). This ensemble learning strategy can provide more robust and s...

Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering.

IEEE transactions on cybernetics
Clustering techniques attempt to group objects with similar properties into a cluster. Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention. Graph convolut...

Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability Model for Image Segmentation.

Sensors (Basel, Switzerland)
Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM...

Unsupervised machine learning methods and emerging applications in healthcare.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensiona...

Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering.

The journal of pathology. Clinical research
Deep learning models are increasingly being used to interpret whole-slide images (WSIs) in digital pathology and to predict genetic mutations. Currently, it is commonly assumed that tumor regions have most of the predictive power. However, it is reas...