Kristian Sabo

One-dimensional center-based l 1 -clustering method

Motivated by the method for solving center-based Least Squares—clustering problem (Kogan in Introduction to clustering large and high-dimensional data, Cambridge University Press, 2007; Teboulle in J Mach Learn Res 8:65–102, 2007) we construct a very efficient iterative process for solving a one-dimensional center-based l1—clustering problem, on the basis of which it is possible to determine the optimal partition. We analyze the basic properties and convergence of our iterative process, which converges to a stationary point of the corresponding objective function for each choice of the initial approximation. Given is also a corresponding algorithm, which in only few steps gives a stationary point and the corresponding partition. The method is illustrated and visualized on the example of looking for an optimal partition with two clusters, where we check all stationary points of the corresponding minimizing functional. Also, the method is tested on the basis of large numbers of data points and clusters and compared with the method for solving the center-based Least Squares—clustering problem described in Kogan (2007) and Teboulle (2007).

The best least absolute deviations line-properties and two efficient methods for its derivation

Given a set of points in the plane, the problem of existence and finding the least absolute deviations line is considered. The most important properties are stated and proved and two efficient methods for finding the best least absolute deviations line are proposed. Compared to other known methods, our proposed methods proved to be considerably more efficient.

Analysis of the k-means algorithm in the case of data points occurring on the border of two or more clusters

In this paper, the well-known k-means algorithm for searching for a locally optimal partition of the set A@?R^n is analyzed in the case if some data points occur on the border of two or more clusters. For this special case, a useful strategy by implementation of the k-means algorithm is proposed.

Three points method for searching the best least absolute deviations plane

In this paper a new method for estimation of optimal parameters of a best least absolute deviations plane is proposed, which is based on the fact that there always exists a best least absolute deviations plane passing through at least three different data points. The proposed method leads to a solution in finitely many steps. Moreover, a modification of the aforementioned method is proposed that is especially adjusted to the case of a large number of data and the need to estimate parameters in real time. Both methods are illustrated by numerical examples on the basis of simulated data and by one practical example from the field of robotics.

Center-based l1–clustering method

Abstract In this paper, we consider the l1-clustering problem for a finite data-point set which should be partitioned into k disjoint nonempty subsets. In that case, the objective function does not have to be either convex or differentiable, and generally it may have many local or global minima. Therefore, it becomes a complex global optimization problem. A method of searching for a locally optimal solution is proposed in the paper, the convergence of the corresponding iterative process is proved and the corresponding algorithm is given. The method is illustrated by and compared with some other clustering methods, especially with the l2-clustering method, which is also known in the literature as a smooth k-means method, on a few typical situations, such as the presence of outliers among the data and the clustering of incomplete data. Numerical experiments show in this case that the proposed l1-clustering algorithm is faster and gives significantly better results than the l2-clustering algorithm.

Weighted Median of the Data in Solving Least Absolute Deviations Problems

We consider the weighted median problem for a given set of data and analyze its main properties. As an illustration, an efficient method for searching for a weighted Least Absolute Deviations (LAD)-line is given, which is used as the basis for solving various linear and nonlinear LAD-problems occurring in applications. Our method is illustrated by an example of hourly natural gas consumption forecast.

Searching for a Best Least Absolute Deviations Solution of an Overdetermined System of Linear Equations Motivated by Searching for a Best Least Absolute Deviations Hyperplane on the Basis of Given Data

AbstractWe consider the problem of searching for a best LAD-solution of an overdetermined system of linear equations Xa=z, X∈ℝm×n, m≥n,

Estimating the width of a uniform distribution when data are measured with additive normal errors with known variance

\mathbf{a}\in \mathbb{R}^{n}, \mathbf {z}\in\mathbb{R}^{m}

Border estimation of a two-dimensional uniform distribution if data are measured with additive error

. This problem is equivalent to the problem of determining a best LAD-hyperplane x↦aTx, x∈ℝn on the basis of given data

An approach to cluster separability in a partition

(\mathbf{x}_{i},z_{i}), \mathbf{x}_{i}= (x_{1}^{(i)},\ldots,x_{n}^{(i)})^{T}\in \mathbb{R}^{n}, z_{i}\in\mathbb{R}, i=1,\ldots,m