Information bottleneck based incremental fuzzy clustering for large biomedical data

Liu, Yongli; Wei, Xing

doi:10.1016/j.jbi.2016.05.009

Cited by 13 publications

(7 citation statements)

References 17 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, these clustering techniques may not scale to large datasets because of their computational time [24] and low accuracy. Therefore, many cluster analysis techniques are being developed for specific practical problems [25,26], such as finding classes of genes that have similar functions, grouping information on the Internet for different specific queries, and clustering biomedical data [27,28]. Likewise, new clustering approaches specific to visual domains in high dimensional space are required in order to produce better results.…”

Section: Cluster Analysismentioning

confidence: 99%

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Kuru

Khan

2018

Applied Soft Computing

View full text Add to dashboard Cite

Current widely employed clustering approaches may not yield satisfactory results with regard to the characteristics and distribution of datasets and number of clusters to be sought, especially for visual domains in multidimensional space. This study establishes a novel clustering methodology using a pairwise similarity matrix, Clustering Visual Objects in Pairwise Similarity Matrix (CVOIPSM), for grouping similar objects in specific visual domains. A dimensionality reduction and feature extraction technique, along with a distance measuring method and a newly established algorithm, Clustering in Pairwise Similarity Matrix (CIPSM), are combined to develop the CVOIPSM methodology. CIPSM utilizes both Rk-means and an agglomerative, contractible, expandable (ACE) technique to calculate a membership degree based on maximizing inter-class similarity and minimizing intra-class similarity. CVOIPSM has been tested on several datasets, with average success rates on downsized subsamples between 87.5% and 97.75% and between 81% and 87% on the larger datasets. The difference in the success rates for small and large datasets is not statistically significant (p>0.01). Moreover, this method automatically determines the likely number of clusters without any user dictation. The empirical results and the statistical significance test on these results ensure that CVOIPSM performs effectively and efficiently on specific visual domains, disclosing the interrelated patterns of similarities among objects.

show abstract

Section: Cluster Analysismentioning

confidence: 99%

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Kuru

Khan

2018

Applied Soft Computing

View full text Add to dashboard Cite

show abstract

“…One method is a modification of the existing FCM-based incremental clustering, while the other is incremental clustering, i.e., Single - Pass or Online , with weighted fuzzy co-clustering. In 2016, we proposed two incremental algorithms based on information bottleneck, Single - Pass fuzzy c-means (spFCM-IB) and Online fuzzy c-means (oFCM-IB) [ 9 ], which modifies conventional algorithms by considering different weights for each centroid and object and scoring mutual information loss to measure the distance between centroids and objects.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The significant difference between spFDTW and oFDTW lies in the way in which the centroids of each chunk are handled. Similarly to some incremental clustering algorithms [ 9 ], the large-scale time series data will be split into a set of chunks, and each chunk has its own number of objects. In our work, let us suppose there are M chunks in total, which are available in turn.…”

Section: Dtw Based Incremental Fuzzy C Medoids Clusteringmentioning

confidence: 99%

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Liu

Chen

et al. 2018

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

show abstract

“…By introducing Takagi-Sugeno-Kang (TSK) models, the application of fuzzy theory in system control shows a remarkable growth in popularity [3,4]. The fuzzy models are used in a wide variety of applications such as robotics [5], biomedical engineering [6] and decision making [7].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Design of Stabilizer Controller for Interval Type-2 TSK Fuzzy Logic Systems

Pour

Ghaemi

Badamchizadeh

2018

Fuzzy Information and Engineering

View full text Add to dashboard Cite

This paper proposes a new method for designing a state feedback controller for stabilizing and also making the desired performance of Interval Type-2 Takagi-Sugeno-Kang Fuzzy Logic Systems (fIT2 TSK FLS). By using the advantages of WU-Mendel Uncertainty Bounds (WM-UB) method, this paper proposes a new approach for Single-Input and Single-Output (SISO) and Multi-Input and Multi-Output (MIMO) interval type-2 TSK systems, which is based on the Hybrid Compensation Control (HCC) approach. The advantages of this method are no necessity to solving any Linear Matrix Inequalities (LMIs) to find a quadratic Lyapunov function for designing the stabilizer controller and also, the designed controller could compensate the time-varying variations. It should be noted that the inference engine is formulated in closed form and does not require using any type of reduction. Some examples have been conducted in our study to demonstrate the effectiveness of the proposed control design approach and compare this method with the previous approaches. All results illustrate good control performance.

show abstract

Information bottleneck based incremental fuzzy clustering for large biomedical data

Cited by 13 publications

References 17 publications

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

A Systematic Design of Stabilizer Controller for Interval Type-2 TSK Fuzzy Logic Systems

Contact Info

Product

Resources

About