Clustering and a Dissimilarity Measure for Methadone Dosage Time Series
نویسندگان
چکیده
In this work we analyze data for 314 participants of a methadone study over 180 days. Dosages in mg were converted for better interpretability to seven categories in which six categories have an ordinal scale for representing dosages and one category for missing dosages. We develop a dissimilarity measure and cluster the time series using “partitioning around medoids” (PAM). The dissimilarity measure is based on assessing the interpretative dissimilarity between categories. It quantifies the structure of the categories which is partly categorical, partly ordinal and also involves quantitative information. The principle behind the measure can be used for other applications as well, in which there is more information about the meaning of categories than just that they are “ordinal” or “categorical”.
منابع مشابه
Common Dissimilarity Measures are Inappropriate for Time Series Clustering
Clustering algorithms have been actively used to identify similar time series, providing a better understanding of data. However, common clustering dissimilarity measures disregard time series correlations, yielding poor results. In this paper, we introduce a dissimilarity measure based on series partial autocorrelations. Experiments compare hierarchical clustering algorithms using the common d...
متن کاملAn Empirical Comparison of Distance Measures for Multivariate Time Series Clustering
Multivariate time series (MTS) data are ubiquitous in science and daily life, and how to measure their similarity is a core part of MTS analyzing process. Many of the research efforts in this context have focused on proposing novel similarity measures for the underlying data. However, with the countless techniques to estimate similarity between MTS, this field suffers from a lack of comparative...
متن کاملClustering Symbolic Time-Series using L-tuples
Among the many dimensionality reduction methods for timeseries data, Symbolic Aggregate approXimation (SAX) is perhaps the most popular due to its simplicity and uniqueness. With SAX, time-series data can be represented as string sequences which enables the utilization of methods found in text mining and bioinformatics to enhance data mining tasks. We propose an application of L-tuples to impro...
متن کاملخوشهبندی دادههای بیانژنی توسط عدم تشابه جنگل تصادفی
Background: The clustering of gene expression data plays an important role in the diagnosis and treatment of cancer. These kinds of data are typically involve in a large number of variables (genes), in comparison with number of samples (patients). Many clustering methods have been built based on the dissimilarity among observations that are calculated by a distance function. As increa...
متن کاملA Hybrid Time Series Clustering Method Based on Fuzzy C-Means Algorithm: An Agreement Based Clustering Approach
In recent years, the advancement of information gathering technologies such as GPS and GSM networks have led to huge complex datasets such as time series and trajectories. As a result it is essential to use appropriate methods to analyze the produced large raw datasets. Extracting useful information from large data sets has always been one of the most important challenges in different sciences,...
متن کامل