Temat: clustering - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: DSMK-means “density-based split-and-Merge K-means clustering algorithm
Autorzy:: Aldahdooh, R. T.
Ashour, W.
Tematy:: clustering
K-means
Density-based Split
Merge K-means clustering Algorithm
DSMK-means
clustering algorithm; Pokaż więcej
Data publikacji:: 2013
Powiązania:: https://bibliotekanauki.pl/articles/91719.pdf Link otwiera się w nowym oknie
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 51-71
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Opis:: Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. This paper introduces Density-based Split- and -Merge K-means clustering Algorithm (DSMK-means), which is developed to address stability problems of standard K-means clustering algorithm, and to improve the performance of clustering when dealing with datasets that contain clusters with different complex shapes and noise or outliers. Based on a set of many experiments, this paper concluded that developed algorithms “DSMK-means” are more capable of finding high accuracy results compared with other algorithms especially as they can process datasets containing clusters with different shapes, densities, or those with outliers and noise.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Clustering large-scale data based on modified affinity propagation algorithm
Autorzy:: Serdah, A. M.
Ashour, W. M.
Tematy:: clustering
clustering algorithm
data clustering algorithm
propagation algorithm
Affinity Propagation
AP
klasteryzacja
algorytm klastrowania
algorytm propagacji; Pokaż więcej
Data publikacji:: 2016
Powiązania:: https://bibliotekanauki.pl/articles/91694.pdf Link otwiera się w nowym oknie
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 1; 23-33
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Opis:: Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering algorithm is performed on all local cluster exemplars to select well-suited global exemplars of the whole data set. Finally, all the data points are clustered by the similarity between all global exemplars and each data point. Results show that the proposed clustering method can significantly reduce the clustering time and produce better clustering result in a way that is more effective and accurate than AP, KAP, and HAP algorithms.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Leach and heed clustering algorithms in wireless sensor networks: a qualitative study
Autorzy:: Kazerooni, A A
Jelodar, H
Aramideh, J
Tematy:: wireless sensor networks
clustering algorithm
HEED
LEACH; Pokaż więcej
Data publikacji:: 2015
Powiązania:: https://bibliotekanauki.pl/articles/958002.pdf Link otwiera się w nowym oknie
Źródło:: Advances in Science and Technology. Research Journal; 2015, 9, 25; 7-11
2299-8624
Pojawia się w:: Advances in Science and Technology. Research Journal
Opis:: Wireless sensor network consists of numerous small and low cost sensors which collect and transmit environmental data. These nodes are spatially distributed and capable of measuring their ambient. Sensor node is responsible for collecting data in regular intervals, converting the obtained data into electronic signals and transmitting data to sink node or base station through reliable wireless communications. Moreover, these nodes are supplied by non-rechargeable batteries with limited energy. Lifetime and network coverage are crucial factors in WSNs. Thus, particular algorithms must be employed so that energy consumption is reduced. In this paper two clustering algorithms LEACH and HEED are investigated.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Research on ship trajectory extraction based on multiattribute DBSCAN optimisation algorithm
Autorzy:: Xu, Xiaofeng
Cui, Deqaing
Li, Yun
Xiao, Yingjie
Tematy:: clustering algorithm
abnormal route
DBSCAN
feature trajectory extraction
fitting analysis; Pokaż więcej
Data publikacji:: 2021
Powiązania:: https://bibliotekanauki.pl/articles/1551877.pdf Link otwiera się w nowym oknie
Źródło:: Polish Maritime Research; 2021, 1; 136-148
1233-2585
Pojawia się w:: Polish Maritime Research
Opis:: With the vigorous development of maritime traffic, the importance of maritime navigation safety is increasing day by day. Ship trajectory extraction and analysis play an important role in ensuring navigation safety. At present, the DBSCAN (density-based spatial clustering of applications with noise) algorithm is the most common method in the research of ship trajectory extraction, but it has shortcomings such as missing ship trajectories in the process of trajectory division. The improved multi-attribute DBSCAN algorithm avoids trajectory division and greatly reduces the probability of missing sub-trajectories. By introducing the position, speed and heading of the ship track point, dividing the complex water area and vectorising the ship track, the function of guaranteeing the track integrity can be achieved and the ship clustering effect can be better realised. The result shows that the cluster fitting effect reaches up to 99.83%, which proves that the multi-attribute DBSCAN algorithm and cluster analysis algorithm have higher reliability and provide better theoretical guidance for the analysis of ship abnormal behaviour.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: GrDBSCAN: A granular density-based clustering algorithm
Autorzy:: Suchy, Dawid
Siminski, Krzysztof
Tematy:: granular computing
DBSCAN
clustering algorithm
GrDBSCAN
przetwarzanie ziarniste
algorytm grupowania; Pokaż więcej
Data publikacji:: 2023
Powiązania:: https://bibliotekanauki.pl/articles/15548018.pdf Link otwiera się w nowym oknie
Źródło:: International Journal of Applied Mathematics and Computer Science; 2023, 33, 2; 297--312
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Opis:: Density-based spatial clustering of applications with noise (DBSCAN) is a commonly known and used algorithm for data clustering. It applies a density-based approach and can produce clusters of any shape. However, it has a drawback-its worst-case computational complexity is O(n2) with regard to the number of data items n. The paper presents GrDBSCAN: a granular modification of DBSCAN with reduced complexity. The proposed GrDBSCAN first granulates data into fuzzy granules and then runs density-based clustering on the resulting granules. The complexity of GrDBSCAN is linear with regard to the input data size and higher only for the number of granules. That number is, however, a parameter of the GrDBSCAN algorithm and is (significantly) lower than that of input data items. This results in shorter clustering time than in the case of DBSCAN. The paper is accompanied by numerical experiments. The implementation of GrDBSCAN is freely available from a public repository.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: An outlier-robust neuro-fuzzy system for classification and regression
Autorzy:: Siminski, Krzysztof
Tematy:: outliers
neuro-fuzzy system
clustering algorithm
regression
wyjątki
system neurorozmyty
algorytm grupowania; Pokaż więcej
Data publikacji:: 2021
Powiązania:: https://bibliotekanauki.pl/articles/1838201.pdf Link otwiera się w nowym oknie
Źródło:: International Journal of Applied Mathematics and Computer Science; 2021, 31, 2; 303-319
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Opis:: Real life data often suffer from non-informative objects—outliers. These are objects that are not typical in a dataset and can significantly decline the efficacy of fuzzy models. In the paper we analyse neuro-fuzzy systems robust to outliers in classification and regression tasks. We use the fuzzy c-ordered means (FCOM) clustering algorithm for scatter domain partition to identify premises of fuzzy rules. The clustering algorithm elaborates typicality of each object. Data items with low typicalities are removed from further analysis. The paper is accompanied by experiments that show the efficacy of our modified neuro-fuzzy system to identify fuzzy models robust to high ratios of outliers.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Customer’s Purchase Prediction Using Customer Segmentation Approach for Clustering of Categorical Data
Autorzy:: Singh, Juhi
Mittal, Mandeep
Tematy:: categorical data
clustering algorithm
frequent pattern mining
association rules
customer relationship management; Pokaż więcej
Data publikacji:: 2020
Powiązania:: https://bibliotekanauki.pl/articles/1841413.pdf Link otwiera się w nowym oknie
Źródło:: Management and Production Engineering Review; 2021, 12, 2; 57-64
2080-8208
2082-1344
Pojawia się w:: Management and Production Engineering Review
Opis:: Traditional clustering algorithms which use distance between a pair of data points to calculate their similarity are not suitable for clustering of boolean and categorical attributes. In this paper, a modified clustering algorithm for categorical attributes is used for segmentation of customers. Each segment is then mined using frequent pattern mining algorithm in order to infer rules that helps in predicting customer’s next purchase. Generally, purchases of items are related to each other, for example, grocery items are frequently purchased together while electronic items are purchased together. Therefore, if the knowledge of purchase dependencies is available, then those items can be grouped together and attractive offers can be made for the customers which, in turn, increase overall profit of the organization. This work focuses on grouping of such items. Various experiments on real time database are implemented to evaluate the performance of proposed approach.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Usage of the graph clustering algorithm to the recognition of geotechnical layers
Autorzy:: Rabarijoely, S.
Bilski, P.
Falkowski, T.
Tematy:: soil layer
artificial intelligence
DMT test
clustering algorithm
geotechnical layer
recognition
soil type; Pokaż więcej
Data publikacji:: 2007
Powiązania:: https://bibliotekanauki.pl/articles/81821.pdf Link otwiera się w nowym oknie
Źródło:: Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation; 2007, 38; 57-67
0208-5771
Pojawia się w:: Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation
Opis:: The aim of the paper is to present the approach to the application of the graph clustering algorithm to the recognition of geotechnical layers from the dilatometer tests. Results of the measurements obtained from the DMT test in the test site (subsoil of one of the buildings in the Warsaw University of Life Sciences campus) were analyzed by the clustering algorithm which was able to extract the separate groups of the measurements, representing identical soil type. This method is parameterized, so its verifi cation by the geotechnical experts was necessary to determine the optimal parameter values. They lead to the determination of the soil types as close to the actual situation, as possible. Also, the output of the algorithm was analyzed by the geotechnical experts to identify and label the extracted soil types.
W artykule przedstawiono zastosowanie opartej na algorytmie clusteringu grafowego do rozpoznania warstw gruntu na podstawie badań dylatometrycznych. Wyniki pomiarów uzyskiwane dla podłoża jednego z budynków na terenie kampusu SGGW zostały przeanalizowane przez algorytm, który wyodrębnił grupy pomiarów charakterystyczne dla określonych rodzajów gruntów. Przeprowadzona analiza umożliwiała określenie optymalnych wartości parametrów pozwalających pogrupować pomiary i wyodrębnić najbliższe rodzaje gruntów. Ponadto, wyniki działania algorytmu zostały przeanalizowane przez geotechników w celu weryfi kacji identyfi kacji poszczególnych rodzajów gruntów wskazanych przez metodę.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Segmentation algorithm of coal slurry foam with double-point directional extension based on the improved FCM clustering algorithm
Autorzy:: Wu, ZiHao
Huang, XianWu
Shang, HaiLi
Zhao, YuHong
Tematy:: flotation
slime foam segmentation
FCM
clustering algorithm
double-point directional extension
segmentation effect judgment standard; Pokaż więcej
Data publikacji:: 2023
Powiązania:: https://bibliotekanauki.pl/articles/2200332.pdf Link otwiera się w nowym oknie
Źródło:: Physicochemical Problems of Mineral Processing; 2023, 59, 1; art. no. 158850
1643-1049
2084-4735
Pojawia się w:: Physicochemical Problems of Mineral Processing
Opis:: In flotation production, the visual surface information of the flotation foam reflects the flotation effects, which are closely related to the flotation conditions and directly reflect the degree of mineralization of the foam layer. In this study, it was proposed a novel and efficient segmentation algorithm to extract the edge information of slime bubbles, as the boundaries are typically blurred and difficult to segment, due to the slime bubbles sticking to each other in the slime flotation foam image. First, the improved clustering algorithm and image morphology operation were used to extract the edges of the foam spots. Second, the image morphological operations were used as a starting point to look around the foam edge points. The pseudo-edge points were then removed using a region and spatial removal algorithm. Finally, the foam edges were extracted using the double-point directed expansion algorithm. A new criterion was proposed for segmentation effect determination based on the particularity of the segmented object. The feasibility and effectiveness of the foam segmentation method were investigated by comparative experiments. The experimental results showed that the proposed algorithm could obtain the foam surface properties more accurately and provide effective guidance for flotation production.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Molecular characterization of Iranian black cumin (Nigella sativa L.) accessions using RAPD marker
Autorzy:: Neghab, M.G.
Panahi, B.
Tematy:: black cumin
Nigella sativa
Iranian black cumin
Ranunculaceae
flowering plant
RAPD marker
genetic variation
polymorphism
UPGMA method
clustering algorithm
cluster analysis; Pokaż więcej
Data publikacji:: 2017
Powiązania:: https://bibliotekanauki.pl/articles/81098.pdf Link otwiera się w nowym oknie
Źródło:: BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology; 2017, 98, 2
0860-7796
Pojawia się w:: BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology
Dostawca treści:: Biblioteka Nauki

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