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- Gil David, Amir Averbuch
- Pattern Recognition
- 2012

Data clustering is a common technique for data analysis, which is used in many fields, including machine learning, data mining, customer segmentation, trend analysis, pattern recognition and image analysis. Although many clustering algorithms have been proposed, most of them deal with clustering of one data type (numerical or nominal) or with mix data type… (More)

a r t i c l e i n f o a b s t r a c t Data clustering is a common technique for data analysis. It is used in many fields including machine learning, data mining, customer segmentation, trend analysis, pattern recognition and image analysis. The proposed Localized Diffusion Folders (LDF) methodology, whose localized folders are called diffusion folders (DF),… (More)

Recently, the "hot hand" phenomenon regained interest due to the availability and accessibility of large scale data sets from the world of sports. In support of common wisdom and in contrast to the original conclusions of the seminal paper about this phenomenon by Gilovich, Vallone and Tversky in 1985, solid evidences were supplied in favor of the existence… (More)

—This paper shows a novel concept of using diffusion maps for dimensionality reduction when tracing problems in 3G radio networks. The main goal of the study is to identify abnormally behaving base station from a large set of data and find out reasons why the identified base stations behave differently. The paper describes an algorithm consisting of… (More)

- Dudu Lazarov, Gil David, Amir Averbuch
- 2009

Finding useful related patterns in a dataset is an important task in many interesting applications. In particular, one common need in many algorithms, is the ability to separate a given dataset into a small number of clusters. Each cluster represents a subset of data-points from the dataset, which are considered similar. In some cases, it is also necessary… (More)

- Gil David, Amir Averbuch, Ronald R. Coifman
- AAAI Fall Symposium: Manifold Learning and Its…
- 2010

We present a short introduction to an hierarchical clustering method of high-dimensional data via localized diffusion folders.

- Tiina Kovanen, Gil David, Timo Hämäläinen
- NEW2AN
- 2016

a r t i c l e i n f o a b s t r a c t Data-analysis methods nowadays are expected to deal with increasingly large amounts of data. Such massive datasets often contain many redundancies. One effect from these redundancies is the high dimensionality of datasets, which is handled by dimensionality reduction techniques. Another effect is the duplicity of very… (More)

Introduction: We propose a simple, workable algorithm that provides assistance for interpreting any set of data from the screen of a blood analysis with high accuracy, reliability, and inter-operability with an electronic medical record. This has been made possible at least recently as a result of advances in mathematics, low computational costs, and rapid… (More)

Data clustering is a common technique for statistical data analysis. It is used in many fields including machine learning, data mining, customer segmentation, trend analysis, pattern recognition and image analysis. The proposed Localized Diffusion Folders methodology performs hierarchical clustering and classification of high-dimensional datasets. The… (More)