DECODE: a new method for discovering clusters of different densities in spatial data

When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted...

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Bibliographic Details
Published in:Data mining and knowledge discovery 2009-06, Vol.18 (3), p.337-369
Main Authors: Pei, Tao, Jasra, Ajay, Hand, David J., Zhu, A.-Xing, Zhou, Chenghu
Format: Article
Language:English
Subjects:
Aftershocks
Algorithms
Artificial Intelligence
Chemistry and Earth Sciences
Clustering
Computer Science
Data mining
Data Mining and Knowledge Discovery
Datasets
Earthquakes
Geography
Information Storage and Retrieval
Methods
Noise
Physics
Spatial data
Statistics for Engineering
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Summary:When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted a model-based approach, but is either incapable of estimating the thresholds in an automatic way, or limited to only two point processes, i.e. noise and clusters with the same density. In this paper, we present a new density-based cluster method (DECODE), in which a spatial data set is presumed to consist of different point processes and clusters with different densities belong to different point processes. DECODE is based upon a reversible jump Markov Chain Monte Carlo (MCMC) strategy and divided into three steps. The first step is to map each point in the data to its m th nearest distance, which is referred to as the distance between a point and its m th nearest neighbor. In the second step, classification thresholds are determined via a reversible jump MCMC strategy. In the third step, clusters are formed by spatially connecting the points whose m th nearest distances fall into a particular bin defined by the thresholds. Four experiments, including two simulated data sets and two seismic data sets, are used to evaluate the algorithm. Results on simulated data show that our approach is capable of discovering the clusters automatically. Results on seismic data suggest that the clustered earthquakes, identified by DECODE, either imply the epicenters of forthcoming strong earthquakes or indicate the areas with the most intensive seismicity, this is consistent with the tectonic states and estimated stress distribution in the associated areas. The comparison between DECODE and other state-of-the-art methods, such as DBSCAN, OPTICS and Wavelet Cluster, illustrates the contribution of our approach: although DECODE can be computationally expensive, it is capable of identifying the number of point processes and simultaneously estimating the classification thresholds with little prior knowledge.
ISSN:1384-5810
1573-756X
DOI:10.1007/s10618-008-0120-3