Golay Code Transformations for Ensemble Clustering in Application to Medical Diagnostics

Abstract

Abstract-Clinical Big Data streams have accumulated largescale multidimensional data about patients' medical conditions and drugs along with their known side effects. The volume and the complexity of this Big Data streams hinder the current computational procedures. Effective tools are required to cluster and systematically analyze this amorphous data to perform data mining methods including discovering knowledge, identifying underlying relationships and predicting patterns. This paper presents a novel computation model for clustering tremendous amount of Big Data streams. The presented approach is utilizing the error-correction Golay Code. This clustering methodology is unique. It outperforms all other conventional techniques because it has linear time complexity and does not impose predefined cluster labels that partition data. Extracting meaningful knowledge from these clusters is an essential task; therefore, a novel mechanism that facilitates the process of predicting patterns and likelihood diseases based on a semi-supervised technique is presented.

An Efficient DNA Molecule Clustering using GCC Algorithm

“…The aim of these calculations is to label clustered vectors. The label of the vector is basically the exact label of the nearest center, as long as the Hamming distance does not exceed a certain number [6]. The next step is to label clusters depending on the majority of vector labels within the cluster.…”

“…A genetic mutation is a change in DNA basepair sequence [4]. This paper presents a clustering technique that is based on a reverse of the traditional error-correction scheme using the perfect Golay code described in [5] and [6]. Our presented clustering algorithm proposes utilizing the Golay Code error correction algorithm to create hash indices.…”

“…This will come helpful in identifying not only exact matches, but also similar patterns up to a certain similarity threshold. As mentioned in [6], the algorithm will identify two vectors V1 and V2 to be belonging to the same cluster if they are 65.2% up to 100% similar.…”

“…The Hamming distance, a natural similarity measure on binary codes, requires a few machine instructions for each comparison. Moreover, in practical performing exact nearest neighbor search in Hamming space significantly faster than linear search, with sub linear run-times [6].…”

A Review of design of Binary Golay Code and Extended Binary Golay Code for error correction

“…[10] observe the efficiency of turbo code (BPTC) and simulated its efficiency, used the Hamming (15,11) and Hamming (13,9) block channel code combinations and block interleaving to construct a BPSK modulation and BPTC coding system in the concept of feedback encoding in turbo code. This study also analyzed and simulated special combination of MSK modulation with Golay.…”