SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection
Abstract This paper presents a novel anomaly and outlier detection algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This algorithm leverages the concept of similarity and higher-order interactions across multiple subspaces to identify outliers....
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01122-9 |
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| author | M. Z. Naser Ahmad Z. Naser |
| author_facet | M. Z. Naser Ahmad Z. Naser |
| author_sort | M. Z. Naser |
| collection | DOAJ |
| description | Abstract This paper presents a novel anomaly and outlier detection algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This algorithm leverages the concept of similarity and higher-order interactions across multiple subspaces to identify outliers. A comprehensive set of experiments was conducted to evaluate the performance of SPINEX. This algorithm was examined against 21 commonly used anomaly detection algorithms, and across 39 synthetic and real datasets from various domains and of a variety of dimensions and complexities. Furthermore, a complexity analysis was carried out to examine the complexity of the proposed algorithm. Our results demonstrate that SPINEX achieves superior performance, outperforms commonly used anomaly detection algorithms, and has moderate complexity (e.g., O(n log n × d)). More specifically, SPINEX was found to rank at the top of algorithms on the synthetic datasets and the 7th on the real datasets. Finally, a demonstration of the explainability capabilities of SPINEX, along with future research needs, is presented. |
| format | Article |
| id | doaj-art-9f29ea12d22e441e85ac0437b1034b39 |
| institution | OA Journals |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-9f29ea12d22e441e85ac0437b1034b392025-08-20T01:54:22ZengSpringerOpenJournal of Big Data2196-11152025-04-0112115510.1186/s40537-025-01122-9SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detectionM. Z. Naser0Ahmad Z. Naser1School of Civil & Environmental Engineering and Earth Sciences (SCEEES), Clemson UniversityDepartment of Mechanical Engineering, University of ManitobaAbstract This paper presents a novel anomaly and outlier detection algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This algorithm leverages the concept of similarity and higher-order interactions across multiple subspaces to identify outliers. A comprehensive set of experiments was conducted to evaluate the performance of SPINEX. This algorithm was examined against 21 commonly used anomaly detection algorithms, and across 39 synthetic and real datasets from various domains and of a variety of dimensions and complexities. Furthermore, a complexity analysis was carried out to examine the complexity of the proposed algorithm. Our results demonstrate that SPINEX achieves superior performance, outperforms commonly used anomaly detection algorithms, and has moderate complexity (e.g., O(n log n × d)). More specifically, SPINEX was found to rank at the top of algorithms on the synthetic datasets and the 7th on the real datasets. Finally, a demonstration of the explainability capabilities of SPINEX, along with future research needs, is presented.https://doi.org/10.1186/s40537-025-01122-9AlgorithmMachine learningBenchmarkingAnomaly detection |
| spellingShingle | M. Z. Naser Ahmad Z. Naser SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection Journal of Big Data Algorithm Machine learning Benchmarking Anomaly detection |
| title | SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection |
| title_full | SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection |
| title_fullStr | SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection |
| title_full_unstemmed | SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection |
| title_short | SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection |
| title_sort | spinex anomaly similarity based predictions with explainable neighbors exploration for anomaly and outlier detection |
| topic | Algorithm Machine learning Benchmarking Anomaly detection |
| url | https://doi.org/10.1186/s40537-025-01122-9 |
| work_keys_str_mv | AT mznaser spinexanomalysimilaritybasedpredictionswithexplainableneighborsexplorationforanomalyandoutlierdetection AT ahmadznaser spinexanomalysimilaritybasedpredictionswithexplainableneighborsexplorationforanomalyandoutlierdetection |