{"title":"Novel approach for nearest neighbor search in high dimensional space","authors":"Ming Zhang, R. Alhajj","doi":"10.1109/IS.2008.4670504","DOIUrl":null,"url":null,"abstract":"Index structures for nearest neighbor search in high-dimensional metric space are mostly built by partitioning the data set based on distances to certain reference point(s). Using the constructed index, the search is limited to a smaller number of the partitions in a way to avoid exhaustive search. However, the approaches already described in the literature either ignore the property of the data distribution or produce non-disjoint partitions; this greatly aspects the search efficiency. In this paper, we propose a new index structure, which overcomes the above disadvantages. The proposed tree structure is constructed by recursively dividing the data set into a nested set of approximate equivalence classes. We also propose a new reference point selection method using principal component analysis (PCA). The conducted analysis and the reported test results demonstrate that the proposed index structure, empowered by the PCA-based reference selection strategy, gives an optimal partition of the data set and greatly improves the search efficiency compared to the VP-tree, which is one of the approaches well documented in the literature.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"00 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 4th International IEEE Conference Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IS.2008.4670504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Index structures for nearest neighbor search in high-dimensional metric space are mostly built by partitioning the data set based on distances to certain reference point(s). Using the constructed index, the search is limited to a smaller number of the partitions in a way to avoid exhaustive search. However, the approaches already described in the literature either ignore the property of the data distribution or produce non-disjoint partitions; this greatly aspects the search efficiency. In this paper, we propose a new index structure, which overcomes the above disadvantages. The proposed tree structure is constructed by recursively dividing the data set into a nested set of approximate equivalence classes. We also propose a new reference point selection method using principal component analysis (PCA). The conducted analysis and the reported test results demonstrate that the proposed index structure, empowered by the PCA-based reference selection strategy, gives an optimal partition of the data set and greatly improves the search efficiency compared to the VP-tree, which is one of the approaches well documented in the literature.