Probabilistic D-clustering

Ben-Israel, Adi
İyigün, Cem
We present a new iterative method for probabilistic clustering of data. Given clusters, their centers and the distances of data points from these centers, the probability of cluster membership at any point is assumed inversely proportional to the distance from (the center of) the cluster in question. This assumption is our working principle.


Principal Coordinate Clustering
SEKMEN, ali; ALDROUBİ, Akram; HAMM, Keaton; Koku, Ahmet Buğra (2017-12-14)
This paper introduces a clustering algorithm, called principal coordinate clustering. It takes in a similarity matrix SW of a data matrix W and computes the singular value decomposition of SW to determine the principal coordinates to convert the clustering problem to a simpler domain. It is a relative of spectral clustering, however, principal coordinate clustering is easier to interpret, and gives a clear understanding of why it performs well. In a fashion, this gives intuition behind why spectral clusteri...
Probabilistic phase based sparse stereo
ULUSOY PARNAS, İLKAY; Halıcı, Uğur; HANCOCK, EDWIN (2004-08-26)
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Multiple Model Adaptive Estimation Algorithm for Systems with Parameter Change
Söken, Halil Ersin (2016-01-01)
This paper presents an autonomous multiple model (AMM) estimation algorithm for systems with sudden parameter changes. Estimates of a bank of Kalman filters (KFs) are merged based on a newly defined likelihood function. The function is composed of two measures, one for weighting the filters during the steady state mode and the other for weighting when there is a change in the parameters. Compared to the interacting multiple model (IMM) method, the KFs do not interact but compete on the basis of the likeliho...
Reevaluating Spectral Partitioning for Unsymmetric Matrices
Oktay, Eda; Manguoğlu, Murat; Yücel, Hamdullah; Department of Scientific Computing (2020-9)
Parallel solutions to scientific problems having graph representation require efficienttasks and partitioning data. In this thesis, various parallel graph partitioning algorithms are studied. While these algorithms are applicable to both directed and undirected graphs, we focus on the directed case whose matrix representations are sparse and unsymmetric arising in linear system of equations representing various application domains such as computational fluid dynamics and thermal problems. Strategies inspec...
A probabilistic multiple criteria sorting approach based on distance functions
ÇELİK, BİLGE; Karasakal, Esra; İyigün, Cem (2015-05-01)
In this paper, a new probabilistic distance based sorting (PDIS) method is developed for multiple criteria sorting problems. The distance to the ideal point is used as a criteria disaggregation function to determine the values of alternatives. These values are used to sort alternatives into the predefined classes. The method also calculates probabilities that each alternative belong to the predefined classes in order to handle alternative optimal solutions. It is applied to five data sets and its performanc...
Citation Formats
A. Ben-Israel and C. İyigün, “Probabilistic D-clustering,” JOURNAL OF CLASSIFICATION, pp. 5–26, 2008, Accessed: 00, 2020. [Online]. Available: