Publication details
Designing Sketches for Similarity Filtering
| Authors | |
|---|---|
| Year of publication | 2016 |
| Type | Article in Proceedings |
| Conference | 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) |
| MU Faculty or unit | |
| Citation | |
| Web | http://ieeexplore.ieee.org/document/7836729/ |
| Doi | http://dx.doi.org/10.1109/ICDMW.2016.0098 |
| Field | Informatics |
| Keywords | Algorithm;Similarity search;Similarity filtering;Bit strings;Sketches;Hamming distance |
| Description | Abstract: The amounts of currently produced data emphasize the importance of techniques for efficient data processing. Searching big data collections according to similarity of data well corresponds to human perception. This paper is focused on similarity search using the concept of sketches – a compact bit string representations of data objects compared by Hamming distance, which can be used for filtering big datasets. The object-to-sketch transformation is a form of the dimensionality reduction and thus there are two basic contradictory requirements: (1) The length of the sketches should be small for efficient manipulation, but (2) longer sketches retain more information about the data objects. First, we study various sketching methods for data modeled by metric space and we analyse their quality. Specifically, we study importance of several sketch properties for similarity search and we propose a high quality sketching technique. Further, we focus on the length of sketches by studying mutual influence of sketch properties such as correlation of their bits and the intrinsic dimensionality of a set of sketches. The outcome is an equation that allows us to estimate a suitable length of sketches for an arbitrary given dataset. Finally, we empirically verify proposed approach on two real-life datasets. |
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