International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-6, April 2020 2263 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: F3879049620/2020©BEIESP DOI: 10.35940/ijitee.F3879.049620 Handwritten Digit Recognition by using Pattern Recognition & Consensus Clustering SubbaRao Gogulamudi, Vital Kumar Pinnela, Lakshmi Sai Tejaswi Pathuri, RamTeja Borra Abstract: In Big Data, Pattern Recognition and Consensus Clustering procedures have developing significance to the scholastic and expert networks. Today there is an extraordinary worry for ordering the information, as information in wrong classification implies incorrect data, which thus results wastage of resources and hurting the association. Example acknowledgment (PR) helps in maintaining a strategic distance from poor order of information by recognizing the right structure of information in dataset. Perceiving an example is the computerized procedure of finding the specific match and regularities of information, which is firmly identified with Artificial Intelligence and Machine Learning. PR goes about as an essential advance to give bunching since it examinations the structure and vector estimation of every character in dataset. Accord Clustering (CC) additionally called as bunching gatherings, assumes a critical job in arranging and keep up in any sort of information. This is a strategy that joins different bunching answers forget steady, precise and novel outcomes. Right now, actualize PR and CC strategies; we use MNIST dataset which is an enormous database of transcribed digits that is regularly utilized for preparing different frameworks in the field of Machine Learning. Keywords: Consensus Clustering, Pattern recognition, MNIST Dataset, Handwritten digit recognition. I. INTRODUCTION A. Patter Recognition(PR) Today’s digital world is filled with Patterns and these pat- terns can either be observed physically or it can be derived mathematically with the help of certain algorithms. Some of the examples of pattern are colours on clothes, speech pat- tern, alphanumerics in data, etc. In computer science, pattern is referred by the value of its vector features. In a simple PR application [11], initially the raw data is processed then the resultant data is converted into a machine understandable format. This involves two major phases of operation i.e., classification of patterns and clustering the patterns. In clas- sification phase, each pattern is assigned an appropriate la- bel based on the abstraction and is controlled by supervised learning. In clustering, the pattern with the same label is partitioned and categorized under one group and is con- trolled by unsupervised learning. Revised Manuscript Received on April 09, 2020. SubbaRao Gogulamudi,CSE Department, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh. Vital Kumar Pinnela,CSE Department, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh. Lakshmi Sai Tejaswi Pathuri,CSE Department, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh. RamTeja Borra,CSE Department, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh. B. B.MNIST Dataset The MNIST dataset (Modified National Institute of Standards and Technology database) is an enormous database of manually written digits that is ordinarily utilized for preparing frameworks. The MNIST database is a subset of an a lot bigger dataset known as the NIST Special Database [9]. This dataset contains both transcribed numerals and letters. It speaks to an a lot bigger and increasingly broad grouping task, alongside the chance of including progressively complex assignments, for example, semantic understandings through words translation. The openness of this MNIST dataset has unquestionably added to its far reaching use. The entire dataset is nearly little (when contrasted with numerous ongoing benchmarking dataset), allowed to access and use, and is encoded and put away in altogether direct way. The encoding doesn't depend on complex stockpiling structure, compressions, or any information design. Hence, it is made extremely simple to access and this dataset can be incorporated from any stage or through any programming dialects. The NIST dataset, by complexity to the MNIST, has stayed hard to access and use. Coming about to the greater expense and the accessibility of storage when it was gathered, the NIST dataset was initially put away in an effective and conservative way. In spite of the fact that source code to get to the information is given, it was exceptionally testing to use on some ongoing processing stages. Thus, the NIST have as of late discharged a second release of the NIST dataset. Be that as it may, the encoding of that datasetstays in the first configuration from which MNIST was extricated. The portrayal of the MNIST dataset is appeared in figure Fig. 1. MNIST Dataset C. Consensus Clustering(CC) As we all know, Clustering defines the process of gathering a specific set of data related to some category and aggregat- ing them with respect to their characteristics. Whereas Con- sensus Clustering (CC) also known as aggregated clustering,