Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data

The applicability and effectiveness of clustering algorithms had unquestioningly benefitted solving various sectors of real-time problems. However, with the changing time, there is a significant change in forms of the data. This paper briefs about the different taxonomies of the clustering algorithm and highlights the frequently used techniques to understand the research popularity. We also discuss the existing direction of the research work and find that still there is a significant amount of open issues when it comes to clustering medical data. We find that existing techniques are quite symptomatic in nature on local problems in clustering while problems associated with complex medical data are yet to be explored by the researchers. We believe that this manuscript will give a good summary of the effectiveness of existing clustering techniques towards medical data as a contribution.

[1]  Jinta Zheng,et al.  A Clustering-Based Automatic Transfer Function Design for Volume Visualization , 2016 .

[2]  Thomas Schultz,et al.  Open-Box Spectral Clustering: Applications to Medical Image Analysis , 2013, IEEE Transactions on Visualization and Computer Graphics.

[3]  Jiamin Li,et al.  Fuzzy Clustering Algorithms — Review of the Applications , 2016, 2016 IEEE International Conference on Smart Cloud (SmartCloud).

[4]  Rubiyah Yusof,et al.  An Efficient Optimization Method for Solving Unsupervised Data Classification Problems , 2015, Comput. Math. Methods Medicine.

[5]  Abu Sayed Md. Latiful Hoque,et al.  Clustering medical data to predict the likelihood of diseases , 2010, 2010 Fifth International Conference on Digital Information Management (ICDIM).

[6]  Zhiqiang Zhang,et al.  Medical image clustering algorithm based on graph entropy , 2015, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[7]  Fella Hachouf,et al.  A new method for finding clusters embedded in subspaces applied to medical tomography scan image , 2012, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA).

[8]  Shiqian Ma,et al.  Sparse Subspace Clustering for Incomplete Images , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[9]  Massimo Panella,et al.  2D hierarchical fuzzy clustering using kernel-based membership functions , 2016 .

[10]  Hayat Al-Dmour,et al.  MR Brain Image Segmentation Based on Unsupervised and Semi-Supervised Fuzzy Clustering Methods , 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[11]  Hamid A. Jalab,et al.  A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique , 2014, TheScientificWorldJournal.

[12]  Marc Sebban,et al.  Supervised spectral subspace clustering for visual dictionary creation in the context of image classification , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[13]  Vipul K. Dabhi,et al.  A survey of document clustering using semantic approach , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[14]  Amir Ahmad Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset , 2016, Scientifica.

[15]  Chinh T. Dang,et al.  Image super-resolution via Dual-Manifold Clustering and Subspace Similarity , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[16]  Charu C. Aggarwal,et al.  Data Clustering: Algorithms and Applications , 2014 .

[17]  C. Karlsson Handbook of Research on Innovation and Clusters : Cases and Policies , 2008 .

[18]  Nor Ashidi Mat Isa,et al.  Adaptive fuzzy-K-means clustering algorithm for image segmentation , 2010, IEEE Transactions on Consumer Electronics.

[19]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[20]  Ramzi A. Haraty,et al.  An Enhanced k-Means Clustering Algorithm for Pattern Discovery in Healthcare Data , 2015, Int. J. Distributed Sens. Networks.

[21]  Zahir Tari,et al.  A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis , 2014, IEEE Transactions on Emerging Topics in Computing.

[22]  Saumitra N. Bhaduri,et al.  Advanced Business Analytics , 2016 .

[23]  Said Esmail El-Khamy,et al.  An efficient brain mass detection with adaptive clustered based fuzzy C-mean and thresholding , 2015, 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[24]  Shiji Song,et al.  Robust K-Median and K-Means Clustering Algorithms for Incomplete Data , 2016 .

[25]  Jagdish N. Sheth,et al.  Cluster analysis and its applications in marketing research / 261 , 1975 .

[26]  John F. Roddick,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[27]  Yen-Wei Chen,et al.  Liver segmentation using superpixel-based graph cuts and restricted regions of shape constrains , 2015, 2015 IEEE International Conference on Image Processing (ICIP).