A Survey of Classification Techniques in the Area of Big Data

International Journal of Modern Trends in Engineering and Research, 2014

Data mining environment produces a large amount of data that need to be analyzed. Using traditional databases and architectures, it has become difficult to process, manage and analyze patterns. To gain knowledge about the Big Data a proper architecture should be understood. Classification is an important data mining technique with broad applications to classify the various kinds of data used in nearly every field of our life. Classification is used to classify the item according to the features of the item with respect to the predefined set of classes. This paper put a light on various classification algorithms including j48, C4.5, Naive Bayes using large dataset.

Advances in Computing and Intelligent Systems, 2020

Traditional tools for mining the big data have become insufficient due to ever-growing data in the world. For handling big data, real-time and distributed processing is adopted. From so many mining tools options, it can be difficult for a researcher to opt an efficient tool. This paper is intended to aid the researcher who understands WEKA but is inexperienced with big data. The preliminary stage of data mining is classification, which categorizes the data into predefined groups. In this paper, WEKA with MOA package is used to classify big data stream with four different classifiers. Performance of these classifiers is analyzed on the basis of accuracy, i.e., correctly and incorrectly classified instances, time taken to test the model, and time taken to build the model. For this particular scenario, obtained results prove that naive Bayes is the most accurate classifier and decision stump is least effective classifier for big data.

International Journal of Knowledge-based and Intelligent Engineering Systems, 2020

Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utiliz...

Due to the huge increase in the size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data puts forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today, there is not only a necessity for efficient data mining techniques to process large volume of data but also a need for a means to meet the computational requirements to process such huge volume of data. The objective of this research is to compare fuzzy and non-fuzzy algorithms in classification of big data, and to provide a comparative study between the results of this study and the methods reviewed in the literature. In this paper, we implemented the Fuzzy K-Nearest Neighbor method as a fuzzy technique and the Support Vector Machine as non- fuzzy technique using the map reduce paradigm to process on big data. Results on different data sets show that the proposed Fuzzy K Nearest Neighbor method outperforms a better performance than the Support Vector Machine and the method reviewed in the literature.

International Journal of E-Health and Medical Communications

In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, and many other social media networks are used to collect the unstructured data. The conversion of unstructured data into structured data or meaningful information is a very tedious task. The different machine learning classification algorithms are used to convert unstructured data into structured data. In this paper, the authors first collect the unstructured research data from a frequently used social media network (i.e., Twitter) by using a Twitter application program interface (API) stream. Secondly, they implement different machine classification algorithms (supervised, unsupervised, and reinforcement) like decision trees (DT), neural networks (NN), support vector machines (SVM), naive Bayes (NB), linear regression (LR), and k-nearest neighbor (K-NN) from the co...

Big Data make conversant with novel technology, skills and processes to your information architecture and the people that operate, design, and utilization them. The big data delineate a holistic information management contrivance that comprise and integrates numerous new types of data and data management together conventional data. The Hadoop is an unlocked source software framework licensed under the Apache Software Foundation, render for supporting data profound applications running on huge grids and clusters, to proffer scalable, credible, and distributed computing. This is invented to scale up from single servers to thousands of machines, every proposition local computation and storage. In this paper, we have endeavored to converse about on the taxonomy for big data and Hadoop technology. Eventually, the big data technologies are necessary in providing more actual analysis, which may leadership to more concrete decision-making consequence in greater operational capacity, cost deficiency, and detect risks for the business. In this paper, we are converse about the taxonomy of the big data and components of Hadoop.

International Journal of Information Systems and Computer Sciences, 2019

Big Data systems are the eventual outcomes of the today's data centric World. Big Data is becoming more popular with the internet technology development, which means the data sets whose size is beyond the ability of current technology, method and theory to capture, manage, and process the data within a tolerable elapsed time. A major source of Big data during the last decade originated from Internet related applications such asSocial media. Twitter is one of the largest social media site, because it is having millions of tweets every day. Twitter tweets are some of the central source of unstructured data and to find meaningful information from unstructured data is very difficult. With the help of classification technique, it is possible to change unstructured data into organised form. In this work, twitter data was taken as the dataset. For training data, Intelligent analysis methods like Random Forest, Logistic Regression and Decision Tree classifier are used. For analysing the data, Python programming was used. The result shows that Random Forest classifieryielded more classification accuracy than Logistic Regression classifier andDecision Tree classifier.

NCICCNDA, 2018

Now a days as Big Data is generated from sensor networks, IOTs, Social network and many collaborative networks, etc. continuously over a period of time line resulting in very large volumes of Data of various types at a phenomenal rate. Handling the characteristics of Big Data is a huge challenge. To handle this large data called big data a different approach or algorithm is required by organizations to handle all the challenges of Big data the Big data system requires a massive processing power and stable complex network configurations. Focuses should be on the analysis part of the big data classification by implementing different techniques in it. The various clustering techniques and the algorithms with the challenges they pose with Big data are discussed. To handle the dimensionality issues and approach the unsupervised machine learning, the use of clustering technique is the most common step in Big Data.

International Journal of Trend in Research and Development,, 2019

Machine learning is a field of computer science which gives computers an ability to learn without being explicitly programmed. Machine learning is used in a variety of computational tasks where designing and programming explicit algorithms with good performance is not easy.Big data are now rapidly expanding in all science and engineering domains. The potentials of these increased volumes of data are obviously very significant to every aspect of our lives. To aid us in decision making and future predictions requires new ways of thinking and new learning techniques to address the various challenges.Traditional analytical approaches are insufficient to analyze big data because they are highly scalable and unstructured data captured in real time. Machine learning (ML), addresses this challenge, by enabling a system to automatically learn patterns from data that can be leveraged in future predictions.This paper reviews some machine learning techniques for big data highlighting their uses/applications, strengths and weaknesses in learning data patterns. The techniques reviewed include Bayesian network, association rules, naïve bayes, decision trees, nearest neighbor and super vector machines (SVM).

Assemblage of large and complex datasets are generally called Big Data. These large datasets cannot be stored, managed or analyzed easily by the current tools and methodologies because of their large size and complexity. However, such datasets provide various opportunities like modelling or predicting model the future. This overwhelming growth of data is now coupled with various new challenges. Increase in data at a massive rate has resulted in most exciting opportunities for researchers in the upcoming years. In this paper, we discuss about the topic in detail sushisen algorithms in HEPMASS dataset, its current scenario, characteristics and challenges to forecast the future. This paper discusses about the tools and technologies used to manage these large datasets and also some of the problems and essential issues such as security management and privacy of data.