Abstract. Finding correlation clusters in the arbitrary subspaces of highdimensional data is an important and a challenging research problem. The current state-of-the-art correlati...
Abstract. Given an arbitrary data set, to which no particular parametrical, statistical or geometrical structure can be assumed, different clustering algorithms will in general pr...
Abstract. To establish lower bounds on the amount of replication, there is a common partition argument used to construct indistinguishable executions such that one violates some pr...
We consider the problem of partitioning n integers chosen randomly between 1 and 2m into two subsets such that the discrepancy, the absolute value of the difference of their sums,...
Traditional similarity or distance measurements usually become meaningless when the dimensions of the datasets increase, which has detrimental effects on clustering performance. I...