Faster Proximity Searching in Metric Data

A number of problems in computer science can be solved efficiently with the so called memory based or kernel methods. Among this problems (relevant to the AI community) are multimedia indexing, clustering, non supervised learning and recommendation systems. The common ground to this problems is satisfying proximity queries with an metric database. In this paper we introduce a new technique for making practical indexes for metric range queries. This technique improves existing algorithms based on pivots and signatures, and introduces a new data structure, the Fixed Queries Trie to speedup metric range queries. The result is an O(n) construction time index, with query complexity O(nα

• Type: Conference
• Year: 2004
• Where: MICAI
• Authors: Edgar Chávez, Karina Figueroa
• Keywords: Artificial Intelligence, Metric Range Queries, MICAI 2004, Non Supervised Learning, Proximity Queries