关键词: 不确定数据, co-location模式, UJoin-based算法, 边界矩形剪枝, 三角不等式剪枝

Abstract: Studied the problem of mining frequent co-location patterns from uncertain data whose locations are described by probability density functions （PDF）. It is showed that the UJoin-based algorithm， which generalizes the Join-based algorithm to handle uncertain instances，is very inefficient. The inefficiency comes from the fact that UJoin-based computes expected distances （ED） between instances. For arbitrary PDF’s， expected distances are computed by numerical integrations， which are costly operations. Various pruning methods are studied to avoid such expensive expected distance calculation. Experiments have been conducted to evaluate the effectiveness of this pruning techniques.

Key words: uncertain data, co-location patterns, UJoin-based algorithm, BR pruning, triangle inequality pruning