Generalized Net of Cluster Analysis Process Using STING: A Statistical Information Grid Approach to Spatial Data Mining

Bureva, Veselina; Sotirova, Evdokia; Popov, Stanislav; Mavrov, Deyan; Traneva, Velichka

doi:10.1007/978-3-319-59692-1_21

Cited by 23 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through the segmentation of complex motions, we have obtained simple motions, but how to classify these motions is still an issue. Common clustering algorithms can be divided into hierarchical method [25]- [28], partition method [29]- [32], density method [33]- [36], grid method [37], [38], statistical method [39]- [42], and neural network method [43], [44]. The hierarchical clustering algorithm has a simple structure, but it is not sensitive to the input order of samples, and the algorithm time complexity is large.…”

Section: Introductionmentioning

confidence: 99%

Motion Recognition Based on Sum of the Squared Errors Distribution

Yang

Cui

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Human motion recognition is playing an increasingly important role in modern society. Direct recognition of complex motions has great limitations, so we usually study basic motions first. The key to the establishment of the basic motion set is how to determine the number of basic motions. Sum of the Squared Errors Distribution (SSED) is therefore proposed to determine the number of clustering classes of Self Organizing Maps (SOM). Secondly, the Weighted Tangent Segmentation (WTS) is also proposed to segment complex motions into simple ones, and the sequence with the time stamp is generated. Finally, Back Propagation with the time stamp (BPTS) is proposed to classify the simple motions according to the basic motion set, and the complex motion is recognized according to the time stamp. The motions of human upper limbs are used to verify the effectiveness of this method. SSED determines that the number of clusters of human upper limb motion is 10. The experimental results show that the correct segmentation rate (CSR) is 98.13%. The recognition rate of basic motions is 98.67% and 99.33% in user independent (UI) and user dependent (UD) experiments, respectively. The recognition rate of complex motions is 96.37%. Experiments on UI and UD recognitions verified the effectiveness of our algorithm. Compared with other recognition algorithms, the complex motion recognition method proposed in this paper has better recognition performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Motion Recognition Based on Sum of the Squared Errors Distribution

Yang

Cui

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It divides data samples into different categories in the pattern space by exploring potential distribution structures of data. In the past decades, many classical clustering algorithms have been proposed, such as K-means, DBSCAN, Gaussian mixture model, spectral clustering, nonnegative matrix factorizationbased clustering, and graph-based clustering [2][3][4][5]. Recently, deep clustering has attracted much attention with the increasing collection of high-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Semisupervised Deep Embedded Clustering with Adaptive Labels

Chen

Gao

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

Deep embedding clustering (DEC) attracts much attention due to its outperforming performance attributed to the end-to-end clustering. However, DEC cannot make use of small amount of a priori knowledge contained in data of increasing volume. To tackle this challenge, a semisupervised deep embedded clustering algorithm with adaptive labels is proposed to cluster those data in a semisupervised end-to-end manner on the basis of a little priori knowledge. Specifically, a deep semisupervised clustering network is designed based on the autoencoder paradigm and deep clustering, which well mine the clustering representation and clustering assignment by preventing the shift of labels in DEC. Then, to train parameters of the deep semisupervised clustering network, a back-propagation-based algorithm with adaptive labels is introduced based on the pretrain and fine-tune strategies. Finally, extensive experiments on representative datasets are conducted to evaluate the performance of the proposed method in terms of clustering accuracy and normalized mutual information. Results show the proposed method outperforms the state-of-the-art methods of DEC.

show abstract

“…With the help of the generalized nets many optimization algorithms were modelled, 3,9,10,14,13,11,21,17 as well as processes taking place in health and education establishments. 1,18,20 A part of the model is connected with intelligent systems as well as neuron nets, 16 genetic and other algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In this article we are observing a common model of the Smart House. 4,10 The GN model 1, 2 will help us to easily and clearly understand the basic operating mode of the communication systems in the Smart House and the possibilities of penetration in the system so that we can improve the security, to remove malfunctions, and analyse the whole process better.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Smart Home Cyber System with Intuitionistic Fuzzy Estimation

Videv¹,

Bozveliev²,

Sotirov³

2019

ISIJ

View full text Add to dashboard Cite

The turbulent development of the digital technologies in the last few years inevitably led to their usage in all areas of life, including our homes. The technological benefits are demonstrated in the control and management of the so called "Smart House." By extending our opportunities with the remote controller, which provides an enormous freedom of the user, there also comes the threat of breaking in and taking control of the "Smart House." This article reviews the cyber threats to the "Smart House" and provides an analysis by using fuzzy estimations for the possible ways of breaking through the system's defence and interfering with its activities. Various options for preventing cyber threats in such environments are also presented.

show abstract

Generalized Net of Cluster Analysis Process Using STING: A Statistical Information Grid Approach to Spatial Data Mining

Cited by 23 publications

References 7 publications

Motion Recognition Based on Sum of the Squared Errors Distribution

Motion Recognition Based on Sum of the Squared Errors Distribution

Semisupervised Deep Embedded Clustering with Adaptive Labels

Modeling of Smart Home Cyber System with Intuitionistic Fuzzy Estimation

Contact Info

Product

Resources

About