New Approach in Big Data Mining for Frequent Itemset Using Mapreduce in HDFS

Nikam, P.V.; Deshpande, Deepa S.

doi:10.1109/i2ct.2018.8529471

Cited by 3 publications

(4 citation statements)

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“…This trend witnessed significant contributions, such as the PFP-Growth algorithm for probabilistic frequent pattern mining. Contemporary Advances in Uncertain Data Handling: With advancements in machine learning and AI, recent years have seen a surge in the development of automated and adaptive algorithms to classify data based on its certainty [13]. Techniques such as probabilistic graphical models, Bayesian networks, and deep learning have been leveraged to navigate the complexities of uncertain databases, offering unparalleled precision and scalability [14].…”

Section: Literature Surveymentioning

confidence: 99%

Modern Approaches to Uncertain Database Exploration from Categorizing Data to Advanced Mining Solutions

Sridevi Malipatil

2023

IJRITCC

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In today's digitized era, the ubiquity of data from diverse sources has introduced unique challenges in database management, notably the issue of data uncertainty. Uncertainty in databases can arise from various factors – sensor inaccuracies, human input errors, or inherent vagueness in data interpretation. Addressing these challenges, this research delves into modern approaches to uncertain database exploration. The paper begins by exploring methods for categorizing data based on certainty levels, emphasizing the importance and mechanisms to distinguish between certain and uncertain data. The discussion then transitions to highlight pioneering mining solutions that enhance the utility of uncertain databases. By integrating state-of-the-art techniques with traditional database management principles, this study aims to bolster the reliability, efficiency, and versatility of data mining in uncertain contexts. The implications of these methods, both theoretically and in real-world applications, hold the potential to redefine how uncertain data is perceived and utilized in diverse sectors, from healthcare to finance.

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Section: Literature Surveymentioning

confidence: 99%

Modern Approaches to Uncertain Database Exploration from Categorizing Data to Advanced Mining Solutions

Sridevi Malipatil

2023

IJRITCC

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“…Finding the frequent itemset is a very essential process in data mining, as stated by Pallavi V. Nikam et al [5] in 2018. Applications such as association rule mining as well as corelations can benefit from the utilisation of these common item collections.…”

Section: Literature Surveymentioning

confidence: 99%

“…It has no information about the other animals in its group. This makes it possible to have a procedure that is both very easy and reliable for rebooting the task in the event that it fails [2,5].…”

Section: Figure 4 Data Flow Of Mapreducementioning

confidence: 99%

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MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

Sujit R Wakchaure

2023

IJRITCC

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One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.

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