The concepts of keypoint extraction and fusion patterns can be applied to various sequential pattern mining problems beyond order-preserving patterns. Some potential applications and adaptations include:
Sequential Event Analysis:
In event sequence analysis, keypoint extraction can help identify critical events or transitions in a sequence, while fusion patterns can capture combined event occurrences. Adapting the concepts for event sequences may involve considering event dependencies, temporal relationships, and event frequency in pattern mining.
Text Mining and Natural Language Processing:
Keypoint extraction can be utilized in text mining to identify significant changes or patterns in textual data, while fusion patterns can capture combined text patterns or phrases. Adapting the concepts for text analysis may involve considering word sequences, semantic relationships, and context-based patterns in text data.
Image and Video Analysis:
Keypoint extraction can play a role in identifying key features or changes in image and video data, while fusion patterns can capture combined visual patterns or object interactions. Adapting the concepts for image and video analysis may involve considering spatial relationships, object recognition, and temporal sequences in visual data patterns.
By applying keypoint extraction and fusion pattern concepts to diverse sequential pattern mining problems, researchers and practitioners can enhance pattern discovery across various data types and domains, enabling more comprehensive and insightful analysis of sequential data.