Locality-Sensitive Hashing for Data with Categorical and Numerical Attributes Using Dual Hashing

Locality-Sensitive Hashing for Data with Categorical and Numerical Attributes Using Dual Hashing

Locality-sensitive hashing techniques have been developed to efficiently handle nearest neighbor searches and similar pair identification problems for large volumes of high-dimensional data. This study proposes a locality-sensitive hashing method that can be applied to nearest neighbor search problems for data sets containing both numerical and categorical attributes. The proposed method makes ...

Beyond Locality-Sensitive Hashing

We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space. For n points in R, our algorithm achieves Oc(n + d logn) query time and Oc(n + d logn) space, where ρ ≤ 7/(8c2) + O(1/c3) + oc(1). This is the first improvement over the result by Andoni and Indyk (FOCS 2006) and the first data structure that bypasses a locality-sensitive hashing lower boun...

Locality-Sensitive Hashing

2 . 3 Sketching using Locality Sensitive Hashing

In this lecture we will get to know several techniques that can be grouped by the general definition of sketching. When using the sketching technique each element is replaced by a more compact representation of itself. An alternative algorithm is run on the more compact representations. Finally, one has to show that this algorithm gives the same result as the original algorithm with high probab...

Frequent-Itemset Mining Using Locality-Sensitive Hashing

The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. We investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations of Apriori that are based on asymmetric LS...