Name
Abstract | ||
|---|---|---|
In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work and that precisely links a hyper-tree structure to the data set assumptions. We show that a set of conditional index invariant hyper graph forms a tree and then, we show that any tree factorization optimizes the conditional probability of an HCRF model. We evaluate our method based on a custom data set that we obtain by running simulations of a time dynamic sensor network. The performance of the proposed method outperforms the existing clustering methods, such as the Girvan-Newmans algorithm, the Kargers algorithm and the Spectral Clustering method, in terms of packet ac ceptance probability and delay. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS | Spectral clustering,Fuzzy clustering,Canopy clustering algorithm,Data mining,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Constrained clustering,Cluster analysis |
DocType | ISSN | Citations |
Conference | 1058-6393 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladislav Vasilev | 1 | 3 | 1.12 |
| Georgi Iliev | 2 | 30 | 8.53 |
| Vladimir Poulkov | 3 | 56 | 20.39 |
| Albena Mihovska | 4 | 125 | 26.27 |