International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 11 | Nov -2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1420 Analysis of Document Clustering using Pseudo Dynamic Quantum Clustering Approach Sahinur Rahman Laskar 1 , Bhagaban Swain 2 1,2 Department of Computer Science & Engineering Assam University, Silchar, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In the field of information processing like data mining, information retrieval, natural language processing and machine learning, Quantum Computing play vital role for extracting the implicit, potentially useful and previously unknown information from huge sets of data. In [1] and [2], proposed two techniques of document ranking and document clustering with Quantum concept. The Quantum Clustering (QC) technique used for information processing which is basically depends on time independent Schrödinger equation for clustering the data and the Dynamic Quantum Clustering (DQC) came into existence when dynamic of the system is computed by means of the time dependent Schrödinger equation. The Dynamic Quantum Clustering (DQC),is a recent clustering technique based on physical perception from quantum mechanics where clusters are computed by means of the time dependent Schrödinger equation and clusters are identified as the minima of the potential function of the Schrödinger equation. In this paper, proposed a novel approach i.e. Pseudo Dynamic Quantum Clustering by considering time dependent Schrödinger equation for clustering the documents such that which provides an agreeable performance in terms of the quality of clusters and the efficiency of the computation in comparison of existing classical approach and earlier proposed approach [2]. Key Words: Clustering; Document Clustering; QC; DQC; Pseudo Dynamic Quantum Clustering. 1.INTRODUCTION The objective of Clustering technique [3],[4] of automatically classify and organizing a large data collection into a finite and discrete set of data rather than provide an accurate characterization of unobserved samples generated from the same probability distribution. This problem is implied in the sense that any given set of objects can be clustered in different ways with no clear criterion for preferring one clustering over another. This challenge influenced researchers to improve classical clustering algorithm and thus Quantum Clustering (QC) technique [5],[6],[7] was developed. The QC is a broad process in the field of Quantum Information Processing (QIP), this approach brings out Schrödinger equation in existence for clustering the data but not on documents. In [2], proposed a technique which mainly focused on document clustering using time independent QC algorithm. The Dynamic Quantum Clustering (DQC) is a recent clustering method based on time dependent Schrödinger equation. This scenario is discussed in the section 3. The DQC and QC are differentiated based on the fact that DQC is computed in dynamic of the system rather than static system. This paper is the extension of the worked [2] i.e. analyses of document clustering using the proposed new approach i.e. Pseudo Dynamic Quantum Clustering. The remainder of the paper is organized as follows. In section 2, a brief description of document clustering concept is presented. Section 3 addresses the method of dynamic quantum clustering based on time dependent Schrödinger equation. The proposed method i.e. Pseudo Dynamic Quantum Clustering is described in section 4. Experiments and results over a standard data set and comparison with existing method is given in section 5 and Finally concluding remarks for future research direction in section 6. 2. DOCUMENT CLUSTERING Document clustering [8], [9] organizes documents into different groups called as clusters, where the documents in each cluster share some common features according to defined similarity measure. Document Clustering is different than document classification based on the fact that in document classification, the classes (and their properties) are known a priori, and documents are assigned to these classes; whereas, in document clustering, the number, properties, or membership (composition) of classes is not known in advance. Thus, classification is an example of supervised machine learning and clustering that of unsupervised machine learning. The well-known existing classical clustering techniques [10] like k-means, Agglomerative hierarchical clustering are available but limited in terms of quality of clusters which leads to the motivation of the development of quantum inspired clustering technique, where clusters are computed through the minima of the potential function of the Schrödinger equation. This scenario will be discussed in the next section.