Mining Contextual Item Similarity without Concept Hierarchy
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2022, 00, pp. 1-8
- Issue Date:
- 2022-02-28
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Mining_Contextual_Item_Similarity_without_Concept_Hierarchy.pdf | Published version | 4.28 MB |
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In the modern era data is precious Therefore a huge amount of data is being generated every moment and data mining extracts insight from this data Item similarity mining is a special domain of data mining that helps discover inherent and important characteristics of a dataset It is a popular research problem with application in numerous domains In this work we propose a novel symmetric null invariant measure of similarity that can evaluate contextual similarity between items without any additional metadata We also propose an optimal algorithm for calculating this measure Moreover as the optimal algorithm has comparatively high runtime complexity we propose a heuristic algorithm which generates an approximate result without sacrificing much accuracy This similarity can be used for mining localized associations and discovering object relationships in large datasets The results obtained using the proposed measure in six real life datasets confirm the measure s effectiveness and versatility in data of varying nature
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