An improvement on quantum clustering

• Published in: 2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS) pp. 01 - 06
• Main Authors: Nabatian, Mehdi, Tanha, Jafar, Ebrahimzadeh, Alireza Rastkar, Samadi, Negin, Razzaghi-Asl, Nazila
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.12.2021
• Subjects: Clustering; Clustering methods; Data structures; Harmonic analysis; Lead; Machin elearning; Quantum mechanics; Signal processing; Supervised learning; Wave functions
• DOI: 10.1109/ICSPIS54653.2021.9729349

Data and patterns are the most important indicators in the world of information. Clustering is one of the best ways to enter the big data world. The main ability of the clustering is to enter the data space and recognize the data structure. Quantum Clustering (QC) is a innovative clustering method that aims to detect the potential components of a data set, based on physical concepts. QC is a new heuristic formulating procedure based on the Schrödinger equation. The main assumption in QC is that the number and location of minimums Schrödinger potential(V) will assign the number and centers of the clusters. In standard QC, first step is to construct the wave function using the Parzen window symmetric estimator, and the next step is to solve the Schrödinger equation for this wave function. These hypotheses lead the clustering problem to solve the Schrödinger equation for an asymmetric harmonic oscillator. In this paper, we improve the results of QC clustering by considering the asymmetric Parzen estimator and solving the Schrödinger equation for the asymmetric harmonic oscillator.