Outlier detection is an important data mining technique. In this article, the triangle inequality of distances is leveraged to design a pre-cutoff value (PCV) algorithm that calculates the outlier... Show More

Outlier detection is an important data mining technique. In this article, the triangle inequality of distances is leveraged to design a pre-cutoff value (PCV) algorithm that calculates the outlier degree pre-threshold without additional distance computations. This algorithm is suitable for accelerating various metric space outlier detection algorithms. Experimental results on multiple real datasets demonstrate that the PCV algorithm reduces the runtime and number of distance computations for the iORCA algorithm by 14.59% and 15.73%, respectively. Even compared to the new high-performance algorithm ADPOD, the PCV algorithm achieves 1.41% and 0.45% reductions. Notably, the non-outlier exclusion for the first data block in the dataset is significantly improved, with an exclusion rate of up to 36.5%, leading to a 23.54% reduction in detection time for that data block. While demonstrating excellent results, the PCV algorithm maintains the data type generality of metric space algorithms.

Due to the pandemic, there has been a drastic change in the advancement of online learning platforms. This article will help us understand the reasons for the increase and decrease of using online... Show More

Due to the pandemic, there has been a drastic change in the advancement of online learning platforms. This article will help us understand the reasons for the increase and decrease of using online learning platforms. Based on the research conducted, it was observed that almost the majority of the students (48.4%) have not completed the enrolled course. Few of the students have come at least halfway (14.5%) to the completion of the course. And the rest of the students (37.1%) responsibly completed the enrolled course; almost half the students who haven't completed the course indicated that the main barrier faced among the students is the lack of interaction (36.7%).

The density local outlier factor algorithm (LOF) needs to calculate the distance matrix for k-nearest neighbor search. The algorithm has high time complexity and is not suitable for the detection of... Show More

The density local outlier factor algorithm (LOF) needs to calculate the distance matrix for k-nearest neighbor search. The algorithm has high time complexity and is not suitable for the detection of large-scale data sets. A local outlier detection algorithm is proposed based on grid query (LOGD). In the algorithm, the k other data points closest to the data point in the target grid must be in the target grid or in the nearest neighboring grid of the target grid, it is used to improve the neighborhood query operation of the LOF algorithm, the calculation amount of the LOF algorithm is reduced in the neighborhood query. Experimental results show that the proposed LODG algorithm can effectively reduce the time of outlier detection under the condition, the detection accuracy of the original LOF algorithm is basically the same.