Oct 11, 2022 · There are several ways to evaluate pattern mining algorithms: 1. Accuracy. The accuracy of a data mining model is a measure of how correctly the model predicts the target values. The accuracy is measured on a test dataset, which is separate from the training dataset that was used to train the model.

Nov 8, 2017 · Before applying any data mining algorithm, dataset undergoes filtering process like removing missing values in data attributes and non-relevant data, thereby empowering the accuracy of classification techniques.

Performance evaluation of the learned/trained data mining algorithm is important in practice. The performance on only training set is not enough for claiming the results rather we need an independent test set. The test set can be generated from the portion of the training set, which is not used for training, also called hold-out set.

In this paper, we present the performance comparison of Apriori and FP-growth algorithms. The performance is analyzed based on the execution time for different number of instances and confidence in Super market data set. These algorithms are presented together with some experimental data.

find out the efficiency of the algorithm and improve the performance by applying data preprocessing techniques and feature selection and also prediction of new class labels. Keywords: Classification, Mining Techniques, Algorithms.

Jan 1, 2013 · In this paper we have worked with different data mining applications and various classification algorithms, these algorithms have been applied on different dataset to find out the...

During the evaluation, the input datasets and the numberof classifier used are varied to measure the performance of Data Mining algorithm. Datasets are varied with mainly typeof class attribute either nominal or numeric.

Jan 30, 2015 · In this paper we analyze the four major clustering algorithms namely Simple K-mean, DBSCAN, HCA and MDBCA and compare the performance of these four clustering algorithms. Performance of these...

Sep 1, 2019 · In this research there is detailed analysis of how data is used and perceived by various data mining algorithms. Mining algorithms like Naïve Bayes, Support Vector Machines, Linear...

We assess the MineBench applications on a 8-way Shared Memory Parallel (SMP) machine and dissect their critical performance attributes. Amid the evaluation, the info datasets and the quantity of processors utilized are shifted to gauge the scalability of the applications in …