Density-based Clustering Approach Research Articles

Looking for natural patterns in analytical data. 2. Tracing local density with OPTICS.

The main principles and the algorithm of a density-based clustering approach, OPTICS, are described, and its unique properties, such as the ability to reveal clusters of arbitrary shapes and different densities, are illustrated on simulated and real spectral and chromatographic data sets. A "reachability plot" visualizing density fluctuations of data in multivariate space and a "color map" relating the original and/or descriptive features with data clustering allow a deeper insight into the data structure and its interpretation in chemical terms.

Looking for natural patterns in data: Part 1. Density-based approach

A density-based unsupervised clustering approach for detecting natural patterns in data (further denoted as NP) is presented, and its performance is illustrated for data sets with different types of clusters. NP works for arbitrary clusters, is a single-scan technique, requires no presumptions regarding data distribution and requires only one input parameter, which describes the minimal number of objects, considered as cluster. Moreover, a comparison of NP with partitioning approaches is demonstrated. NP can be applied not only for data clustering, but also for the identification of outliers.