@inproceedings{10586,
abstract = {We consider the problem of transforming a given graph G_s into a desired graph G_t by applying a minimum number of primitives from a particular set of local graph transformation primitives. These primitives are local in the sense that each node can apply them based on local knowledge and by affecting only its 1-neighborhood. Although the specific set of primitives we consider makes it possible to transform any (weakly) connected graph into any other (weakly) connected graph consisting of the same nodes, they cannot disconnect the graph or introduce new nodes into the graph, making them ideal in the context of supervised overlay network transformations. We prove that computing a minimum sequence of primitive applications (even centralized) for arbitrary G_s and G_t is NP-hard, which we conjecture to hold for any set of local graph transformation primitives satisfying the aforementioned properties. On the other hand, we show that this problem admits a polynomial time algorithm with a constant approximation ratio.},
author = {Scheideler, Christian and Setzer, Alexander},
booktitle = {Proceedings of the 46th International Colloquium on Automata, Languages, and Programming},
keyword = {Graphs transformations, NP-hardness, approximation algorithms},
location = {Patras, Greece},
pages = {150:1----150:14},
publisher = {Dagstuhl Publishing},
title = {{On the Complexity of Local Graph Transformations}},
doi = {10.4230/LIPICS.ICALP.2019.150},
volume = {132},
year = {2019},
}
@inproceedings{2367,
abstract = {One of the most popular fuzzy clustering techniques is the fuzzy K-means algorithm (also known as fuzzy-c-means or FCM algorithm). In contrast to the K-means and K-median problem, the underlying fuzzy K-means problem has not been studied from a theoretical point of view. In particular, there are no algorithms with approximation guarantees similar to the famous K-means++ algorithm known for the fuzzy K-means problem. This work initiates the study of the fuzzy K-means problem from an algorithmic and complexity theoretic perspective. We show that optimal solutions for the fuzzy K-means problem cannot, in general, be expressed by radicals over the input points. Surprisingly, this already holds for simple inputs in one-dimensional space. Hence, one cannot expect to compute optimal solutions exactly. We give the first (1+eps)-approximation algorithms for the fuzzy K-means problem. First, we present a deterministic approximation algorithm whose runtime is polynomial in N and linear in the dimension D of the input set, given that K is constant, i.e. a polynomial time approximation scheme (PTAS) for fixed K. We achieve this result by showing that for each soft clustering there exists a hard clustering with similar properties. Second, by using techniques known from coreset constructions for the K-means problem, we develop a deterministic approximation algorithm that runs in time almost linear in N but exponential in the dimension D. We complement these results with a randomized algorithm which imposes some natural restrictions on the sought solution and whose runtime is comparable to some of the most efficient approximation algorithms for K-means, i.e. linear in the number of points and the dimension, but exponential in the number of clusters.},
author = {BlĂ¶mer, Johannes and Brauer, Sascha and Bujna, Kathrin},
booktitle = {2016 IEEE 16th International Conference on Data Mining (ICDM)},
isbn = {9781509054732},
keyword = {unsolvability by radicals, clustering, fuzzy k-means, probabilistic method, approximation algorithms, randomized algorithms},
location = {Barcelona, Spain},
pages = {805--810},
publisher = {IEEE},
title = {{A Theoretical Analysis of the Fuzzy K-Means Problem}},
doi = {10.1109/icdm.2016.0094},
year = {2016},
}