Wyniki wyszukiwania - BazTech

1

Segmentation algorithm of coal slurry foam with double-point directional extension based on the improved FCM clustering algorithm

Wu ZiHao, Huang XianWu, Shang HaiLi, Zhao YuHong

Physicochemical Problems of Mineral Processing

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2023

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Vol. 59, iss. 1

art. no. 158850

EN

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.

2

GrDBSCAN: A granular density-based clustering algorithm

Suchy Dawid, Siminski Krzysztof

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 2

297--312

EN

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.

3

Research on ship trajectory extraction based on multiattribute DBSCAN optimisation algorithm

Xu Xiaofeng, Cui Deqaing, Li Yun, Xiao Yingjie

Polish Maritime Research

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2021

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nr 1

136--148

EN

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.

4

Customer’s Purchase Prediction Using Customer Segmentation Approach for Clustering of Categorical Data

Singh Juhi, Mittal Mandeep

Management and Production Engineering Review

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2021

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Vol. 12, No. 2

57--64

EN

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.

5

An outlier-robust neuro-fuzzy system for classification and regression

Siminski Krzysztof

International Journal of Applied Mathematics and Computer Science

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2021

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Vol. 31, no. 2

303--319

EN

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.

6

Towards Obtaining Upper Bound on Sensitivity Computation Process for Cluster Validity Measures

Mishra S., Mondal S., Saha S.

Fundamenta Informaticae

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2018

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Vol. 163, nr 4

351--374

EN

Cluster validity indices are proposed in the literature to measure the goodness of a clustering result. The validity measure provides a value which shows how good or bad the obtained clustering result is, as compared to the actual clustering result. However, the validity measures are not arbitrarily generated. A validity measure should satisfy some of the important properties. However, there are cases when in-spite of satisfying these properties, a validity measure is not able to differentiate the two clustering results correctly. In this regard, sensitivity as a property of validity measure is introduced to capture the differences between the two clustering results. However, sensitivity computation is a computationally expensive task as it requires to explore all the possible combinations of clustering results which are very large in number and these are growing exponentially. So, it is required to compute the sensitivity efficiently. As the possible combinations of clustering results grow exponentially, so it is required to first obtain an upper bound on this possible number of combinations which will be sufficient to compute the value of the sensitivity. In this paper, we obtain an upper bound on the number of possible combinations of clustering results. For this purpose, a generic approach which is suitable for various validity measures and a specific approach which is applicable for two validity measures are proposed. It is also shown that this upper bound is sufficient to compute the sensitivity of various validity measures. This upper bound is very less as compared to the total number of possible combinations of clustering results.

7

Emotion Analysis from Speech of Different Age Groups

Palo Hemanta Kumar, Mohanty Mihir Narayan, Chandra Mahesh

Annals of Computer Science and Information Systems

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2017

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Vol. 10

283--287

EN

This Recognition of speech emotion based on suitable features provides age information that helps the society in different ways. As the length and shape of human vocal tract and vocal folds vary with age of the speaker, the area remains a challenge. Emotion recognition system based on speaker's age will help criminal investigators, psychologists and law enforcement agencies in dealing with different segments of the society. Particularly child psychologists, counselors can take timely preventive measures based on such recognition system. The area remains further complex since the recognition system trained for adult users performs poorer when it involves children. This has motivated the authors to move in this direction. A novel effort is made in this work to determine the age of speaker based on emotional speech prosody and clustering them using fuzzy c-means algorithm. The results are promising and we are able to demarcate the emotional utterances based on age.

8

Clustering large-scale data based on modified affinity propagation algorithm

Serdah A. M., Ashour W. M.

Journal of Artificial Intelligence and Soft Computing Research

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2016

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Vol. 6, No. 1

23--33

EN

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.

9

Leach and heed clustering algorithms in wireless sensor networks: a qualitative study

Kazerooni A A, Jelodar H, Aramideh J

Advances in Science and Technology. Research Journal

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2015

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Vol. 9, nr 25

7--11

EN

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.

10

DSMK-means “density-based split-and-Merge K-means clustering algorithm"

Aldahdooh R. T., Ashour W.

Journal of Artificial Intelligence and Soft Computing Research

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2013

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Vol. 3, No. 1

51--71

EN

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.

11

Algorytm klasteryzacji w zastosowaniu do problemu trasowania pojazdów

Ambroziak T., Jachimowski R.

Logistyka

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2012

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nr 2

PL

W artykule scharakteryzowano problematykę klasteryzacji punktów obsługi dla problemu trasowania pojazdów. Przybliżono wybrane metody klasteryzacji opisywane w literaturze przedmiotu. Zaproponowano algorytm klasteryzacji punktów obsługi zmniejszający złożoność obliczeniową problemu trasowania pojazdów.

EN

The article characterizes the problem of service points clustering in vehicle routing problem. Selected clustering methods described in literature are reviewed. New heuristic service points clustering algorithm that reduces the computational complexity of the vehicle routing problem is presented.

12

Combined classifier based on feature space partitioning

Woźniak M., Krawczyk B.

International Journal of Applied Mathematics and Computer Science

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2012

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Vol. 22, no. 4

855-866

EN

This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier, which makes a decision based on a weighted combination of the discriminant functions of the individual classifiers selected for the committee. The weights mentioned above are dependent not only on the classifier identifier, but also on the class number. The proposed approach is based on the results of previous works, where it was proven that such a combined classifier method could achieve significantly better results than simple voting systems. The proposed modification was evaluated through computer experiments, carried out on diverse benchmark datasets. The results are very promising in that they show that, for most of the datasets, the proposed method outperforms similar techniques based on the clustering and selection approach.

13

An unsupervised learning method for comparing the quality of the soft computing algorithms in analog systems diagnostics

Bilski P.

Przegląd Elektrotechniczny

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2010

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R. 86, nr 11a

242-247

EN

The paper presents the method of assessing the difficulty of the analog system for the diagnostics using soft computing algorithms. As the latter exploit knowledge from data sets obtained from simulations of the diagnosed systems, the method estimates the diagnostic difficulty of the system based on the data set analysis. This allows comparison of various systems and diagnostic methods. The versatile method of the data sets’ difficulty based on the graph clustering algorithm is proposed and explained. It is applied to test fuzzy logic and rough sets against the sixth order Butterworth lowpass filter. Conclusions and future prospects supplement the paper.

PL

W artykule przedstawiono metodę oceny trudności diagnostyki systemów analogowych opartej na metodach sztucznej inteligencji. Wykorzystuje ona zbiory danych uzyskiwane w wyniku symulacji diagnozowanych systemów. Możliwe jest dzięki temu porównywanie różnych systemów oraz metod diagnostycznych. Przedstawiona jest metoda oceny oparta na clusteringu grafowym. Następnie przedstawione jest jej wykorzystanie do porównania wyników diagnostyki filtru dolnoprzepustowego Butterwortha szóstego rzędu przy użyciu zbiorów przybliżonych oraz logiki rozmytej. Na końcu umieszczono wnioski oraz rozważania na temat zastosowań metody.

14

Optical feature clustering algorithm for object tracking in image sequences

Pełczyński P., Arriola J. A.

Image Processing & Communications

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2009

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Vol. 14, no 2-3

23-30

EN

The aim of the presented work was the development of software technique for detection and tracking of moving objects in video sequences. It is intended to serve as an automatic video surveillance or traffic control system. Local image features are detected and tracked in the presented system. Two clustering algorithms are utilised for this task succes-fully. Firstly, the QT (Quality Threshold) algorithm has a potential of new object detection. Secondly, modification of a well known K-means algorithm proved its usefulness in tracking moving objects in image sequences. For reduction of the analysed data, corners are detected in consecutive images. Their motion vector and coordinates produce feature vectors for an image classifier. The obtained results show the ability of the proposed technique to detect and track multiple objects on the basis of their local, visual features. No model matching technique was necessary, which simplified overall approach. Comparatively low number of operations, required to perform tracking process, gives the possibility to implement the algorithm in real time on modern graphics processing unit in PC computers.