Due to the vast amount of data available on the internet nowadays, it is necessary to categorise the data, and fast, accurate, and resilient algorithms for data analysis are required. Support vector machines (SVMs) are a form of machine learning technique that is commonly used to solve a variety of statistical learning issues. It's been designed as a reliable categorization tool, and it's especially useful when there's a lot of data. Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn, with the goal of steadily improving accuracy. Algorithms are trained to create classifications by using statistical approaches. These should ideally have an impact on important growth measures. In this study, we found that employing the Support Vector Machine technique provides the best accuracy and efficiency for our dataset. Our work is based on the evaluation of parameters like accuracy, recall and precision.
Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this paper, we develop a new technique for more efficient pattern mining. Our method find frequent 1-itemset and then uses the heap tree sorting we are generating frequent patterns, so that many. We present efficient techniques to implement the new approach.
Nowadays, a huge amount of data is readily available. As a result, analysing this data in order to obtain any useful information and developing an algorithm based on this analysis is critical. Data mining and machine learning can be used to accomplish this. Machine learning is a subset of artificial intelligence that is used to create algorithms based on data patterns and historical data relationships. Machine learning is an area that spans many disciplines. The various applications of machine learning, the needs of machine learning, the various techniques used by machine learning, the various types of problem solving approaches, and the challenges that machine learning faces are all discussed in this paper.
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