This paper formally defines similarities as tolerance relations, which are reflexive and symmetric binary relations. An abstract set with a similarity is called a tolerance space. The training data set in a learning task is a given database of independent identically distributed random pairs (Xi, Yi), where each Xi is a record and Yi is its label: Yi ∈ {0, 1}. The goal of the learning is to des...

Unlike previous research on patient controlled analgesia, this study explores patient demand behavior over time. We apply clustering methods to disclose demand patterns among patients over the first 24h of analgesic medication after surgery. We consider demographic, biomedical, and surgery-related data in statistical analyses to determine predictors for patient demand behavior, and use stepwise...

We present our experience in creating a novel unsupervised clustering algorithm for situation-aware pattern extraction from usage logs. The algorithm automatically estimates nearoptimal number of clusters and cluster centroids. It models situation by taking advantage of sensors. 5-fold cross validations using real-world data show that the algorithm delivers higher accuracy than existing algorit...

We propose a distance between two realizations of a random process where for each realization only sparse and irregularly spaced measurements with additional measurement errors are available. Such data occur commonly in longitudinal studies and online trading data. A distance measure then makes it possible to apply distance-based analysis such as classification, clustering and multidimensional ...

We present a new approach for the empirical investigation of agglomeration patterns. We examine the clustering of manufacturing firms by identifying patterns of spatial network formation that deviate from randomly generated networks. Using firm-level panel data from Vietnam we calculate transitivity, a measure to determine the strength of clustering of manufacturing firms. We then test whether ...

Clustering algorithms have been utilized in a wide variety of application areas. One of these algorithms is the Fuzzy C-Means algorithm (FCM). One of the problems with these algorithms is the time needed to converge. In this paper, a Fast Fuzzy C-Means algorithm (FFCM) is proposed based on experimentations, for improving fuzzy clustering. The algorithm is based on decreasing the number of dista...

In this paper we present a model of a system for integration of an intelligent tutoring system with data mining tools. The purpose of the integration is twofold; a) to power the system adaptability based on clustering and sequential pattern mining, and b) to enable teachers (non-experts in data mining) to use data mining techniques in their web browser on a daily basis, and get useful visualiza...

BACKGROUND The aim of this paper is to empirically identify a treatment-independent statistical method to describe clinically relevant bleeding patterns by using bleeding diaries of clinical studies on various sex hormone containing drugs. METHODS We used the four cluster analysis methods single, average and complete linkage as well as the method of Ward for the pattern recognition in menstru...

While subspace clustering emerged as an application of pattern mining and some of its early advances have probably been inspired by developments in pattern mining, over the years both elds progressed rather independently. In this paper, we identify a number of recent developments in pattern mining that are likely to be applicable to alleviate or solve current problems in subspace clustering and...

Regionalization, or the grouping of objects in space, is a useful tool for organizing, visualizing, and synthesizing the information contained in multivariate spatial data. Landscape pattern indices can be used to quantify the spatial pattern (composition and configuration) of land cover features. Observable patterns can be linked to underlying processes affecting the generation of landscape pa...