An efficient automated incremental density-based algorithm for clustering and classification

Azhir, Elham; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Sharifi, Arash; Darwesh, Aso Mohammad

doi:10.1016/j.future.2020.08.031

Cited by 23 publications

(11 citation statements)

References 31 publications

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“…The improved K‐means based on the type of application has been used to reduce the number of colors. The K‐means algorithm is one of the powerful algorithms for clustering 44,45 . It is criticized in the color reduction literature because of its high computational requirements and initial values dependence.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

Cui

Shi

2021

Color Research & Application

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The carpet industry is no longer a small business done in the villages on a small scale; instead, it has carved an altogether different space, identity, and appreciation for itself in the cosmopolitan world. As computers are is becoming more and more ubiquitous, most industries, including the carpet industry, use computers for quality improvement, accuracy enhancement, speed development, and cost reduction purposes. Unlike traditional carpet maps, many modern maps include images of human faces for hand‐woven carpet tableau. These digital images comprise millions of colors and thousands of pixels, making it practically impossible to construct and weave the carpet in the same dimensions. Many weavers currently use manual and experience‐based methods for reducing the size and number of hues for making a hand‐woven carpet tableau map. Therefore, the outcomes are not the optimal results and can be improved. Also, many color reduction methods do not focus on the hand‐woven carpet tableau map. To overcome these problems and gaps, this research focuses on proposing a new automatic method for reducing the size of color images without compromising facial nuances, lessening the number of colors used while protecting the important areas of the images, and transforming those images into carpet tableau maps. The proposed approach inputs the original color image. It continuously detecting the face and specifying important areas, and finally, outputs carpet tableau map that is proportional to the given dimensions and color count. To evaluate the proposed method, MATLAB, as a powerful simulation tool, was employed. Final results are compared to the existing approaches in terms of face detection, size reduction, and color quantization. The obtained results have shown that the approach improves speed by 39% in face detection and increases the precision of size reduction and color quantization phases. The results have also confirmed that when images of human faces are reduced by a proposed method to form an appropriate image for tableau maps, they are nearly always perceived as more attractive than the reduced faces via traditional methods.

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Section: The Proposed Methodsmentioning

confidence: 99%

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

Cui

Shi

2021

Color Research & Application

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“…k-means clustering has three main issues that most of the recent solutions have been trying to tackle, which are [15,16]: (P1) pre-clustering requirements, which represent the number of clusters and answer the following question "Do the data have clusters? If yes, how many (k)?…”

Section: K-means Clusteringmentioning

confidence: 99%

Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor

Awad

Hamad

2022

Electronics

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Clustering is one of the most significant applications in the big data field. However, using the clustering technique with big data requires an ample amount of processing power and resources due to the complexity and resulting increment in the clustering time. Therefore, many techniques have been implemented to improve the performance of the clustering algorithms, especially for k-means clustering. In this paper, the neural-processor-based k-means clustering technique is proposed to cluster big data by accumulating the advantage of dedicated machine learning processors of mobile devices. The solution was designed to be run with a single-instruction machine processor that exists in the mobile device’s processor. Running the k-means clustering in a distributed scheme run based on mobile machine learning efficiently can handle the big data clustering over the network. The results showed that using a neural engine processor on a mobile smartphone device can maximize the speed of the clustering algorithm, which shows an improvement in the performance of the cluttering up to two-times faster compared with traditional laptop/desktop processors. Furthermore, the number of iterations that are required to obtain (k) clusters was improved up to two-times faster than parallel and distributed k-means.

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“…In reference, 16 the algorithm used in FIDC failed to detect the clusters inside datasets and it was unable to process high‐dimensional datasets effectively. The NSGA‐II‐based density‐based clustering and classification developed in reference 22 minimized processing time as the count of core increased. However, it failed to enhance the quality of clustering and classification. The developed approach was not suitable for large‐scale and very high‐resolution biomedical imaging datasets 23 …”

Section: Motivationmentioning

confidence: 99%

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

Nadendla

Srikrishna

Rao

2022

Concurrency and Computation

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The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback Social Optimization Algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) Algorithm and Social Optimization Algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058.

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An efficient automated incremental density-based algorithm for clustering and classification

Cited by 23 publications

References 31 publications

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

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