A Comparative Analysis of Various Cluster Detection Techniques for Data Mining

2014 ◽  
Author(s):  
Prashant Vats ◽  
Manju Mandot ◽  
Anjana Gosain
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Cluster Detection ◽  
Detection Techniques

Comparative Analysis of Drift Detection Based Adaptive Ensemble Model with Different Drift Detection Techniques

2021 ◽  
Vol 23 (06) ◽  
pp. 49-55
Author(s):  
Sanjeev Kumar ◽  
◽  
Ravendra Singh ◽  
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
False Positive ◽  
Opinion Mining ◽  
Concept Drift ◽  
Ensemble Classifier ◽  
Stream Data ◽  
Detection Techniques ◽  
Stream Data Mining ◽  
Detection Algorithms

Stream data mining is a popular research area these days. The concept drift detection and drift handling are the biggest challenges of stream data mining. Several drift detection algorithms have been developed which can accurately detect various drifts but have the problem of false-positive drift detection. The false-positive drift detection leads to the performance degradation of the classifier because of unnecessary training in between analyses. Classifier ensemble has shown its efficiency for drift detection, drift handling, and classification. But the ensemble classifiers could not detect the exact position of drift occurrence, so it has to update itself at some fixed interval, which leads to an unnecessary computational burden on the system. Combining the drift detection algorithm with an ensemble classifier can improve the performance and also solve the problems of false-positive drift detection and unnecessary updating of the ensemble classifier. In this paper, a model is proposed that creates a weighted adaptive ensemble classifier by updating it only when a drift detection signal is given by the used drift detection method. The proposed model is evaluated on text-based stream data for sentiment analysis and opinion mining with multiple drift detection algorithms and with multiple classification algorithms as base classifiers for the ensemble. A comparative analysis has been done, and the results have shown the efficiency of the proposed models.

scholarly journals A comparative Analysis of Multiple Regression in Data Mining

2017 ◽  
Vol 08 (06) ◽  
pp. 37-40
Author(s):  
PRIYANKA VERMA ◽  
◽  
RAJNI KORI ◽  
SHIV KUMAR ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Multiple Regression

Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

2022 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Ankit Kumar ◽  
Abhishek Kumar ◽  
Ali Kashif Bashir ◽  
Mamoon Rashid ◽  
V. D. Ambeth Kumar ◽  
...  
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Outlier Detection ◽  
Credit Card ◽  
High Dimensional ◽  
Work Efficiency ◽  
Average Value ◽  
Novel Method ◽  
Detection Of Outliers ◽  
Better Than

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

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