Hybridizing Spectral Clustering with Shadow Clustering

  • N. Bala Krishna
  • S. Murali Krishna
  • C. Shoba Bindu
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Clustering can be defined as the process in which partitioning of objects/data points into a group takes place, such that each group consists of homogeneous type of data points and the groups must be disjoint of each other. Spectral clustering is the process in which it generally partitions the data points/objects into clusters such that the members of the cluster should be similar in nature. Shadow clustering is the technique in which it mainly depends on the binary representation of data and it is a systematic one because it follows a particular order for selection of index i such that whenever the main motivation is nothing but minimizing the required quality factor which measures the complexity level of irredundant final positive disjunctive normal form (PDNF), in such a situation the size of the anti-chain is AC and the number of literals is P. The machine vision or machine learning problems need an efficient mechanism for the effective performance of finding or analyzing images. For that, here a hybrid technique is implemented. The hybrid technique is the combination of both spectral and shadow clustering techniques. The proposed system is implemented as hybridization of shadow clustering and spectral clustering for effective performance of finding/analyzing the images in machine vision or machine learning problems.


Image segmentation Clustering Spectral clustering Shadow clustering k-means clustering 


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Copyright information

© The Author(s) 2019

Authors and Affiliations

  • N. Bala Krishna
    • 1
    • 4
  • S. Murali Krishna
    • 2
  • C. Shoba Bindu
    • 3
  1. 1.Sree Vidyanikethan Engineering CollegeTirupatiIndia
  2. 2.SV College of EngineeringTirupatiIndia
  3. 3.JNTUA College of EngineeringAnanthpuramuIndia
  4. 4.JNTU College of Engineering HyderabadHyderabadIndia

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