LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Parameterless Method for Efficiently Discovering Clusters of Arbitrary Shape in Large Datasets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A nonparametric outlier detection for effectively discovering top-n outliers from engineering data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Hi-index | 12.05 |
Innovations in construction equipment using cognitive and automation techniques, as well as computerized equipment Information Management System (IMS) have greatly simplified the equipment operations and management process. Though the data collection, storage and reporting for equipment management are no longer pressing issues for the company equipment manager, data analysis becomes increasingly difficult with large amounts of data, especially for identifying potential problems in operations and management of a large construction fleet. The proposed decision support system for equipment management uses a resolution-based outlier definition and a nonparametric outlier mining algorithm that can automatically detect inconsistent observations from a large equipment dataset, and rank the records based on their degree of inconsistency. The nonparametric outlier mining algorithm demonstrates ease of use, high flexibility and satisfactory results in construction equipment management.