One-dimensional k-center on uncertain data

Improvements on the k-center problem for uncertain data

In real applications, there are situations where we need to model some problems based on uncertain data. This leads us to define an uncertain model for some classical geometric optimization problems and propose algorithms to solve them. In this paper, we study the k-center problem, for uncertain input. In our setting, each uncertain point Pi is located independently from other points in one of ...

Efficient Algorithms for One-Dimensional k-Center Problems

We present an O(n log n) time algorithm for the (weighted) k-center problem of n points on a real line. We show that the problem has an Ω(n log n) time lower bound, and thus our algorithm is optimal. We also show that the problem is solvable in O(n) time in certain special cases. Our techniques involve developing efficient data structures for processing queries that find a lowest point in the c...

K1D: Multivariate Ripley’s K-function for one-dimensional data

Top-k Distance-Based Outlier Detection on Uncertain Data

This paper studies the problem of top-k distance-based outlier detection on uncertain data. In this work, an uncertain object is modelled by a Gaussian probability density function. Since the Naive approach is very expensive due to costly distance function between uncertain objects, a populated-cell list (PC-list) based top-k distance-based outlier detection approach is proposed in this work. W...

Dynamic Structures for Top- k Queries on Uncertain Data

In an uncertain data set S = (S, p, f) where S is the ground set consisting of n elements, p : S → [0, 1] a probability function, and f : S → R a score function, each element i ∈ S with score f(i) appears independently with probability p(i). The top-k query on S asks for the set of k elements that has the maximum probability of appearing to be the k elements with the highest scores in a random ...