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

Abstract

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 Responsible editor: Charu Aggarwal. 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 mth nearest distance, which is referred to as the distance between a point and its mth 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 mth 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.

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“…This is a classic smoothing factor in density estimates whose behavior is well understood, and methods that have an analogous parameter (e.g., Ankerst et al [1999], , Pei et al [2009], and Stuetzle and Nugent [2010]) are typically robust to it.…”

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“…Algorithm 2 gives the pseudocode for finding the optimal solution to (7). Here, we use k for both neighborhood calculation and "noise" threshold as a classic smoothing factor whose effect can be well understood referencing [19,20,30,31].…”

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“…follows a zero mean Gauss distribution, the PHD for the new birth target is (11) diag(100, 25,100, 25) b  P (12) The measurement equation is 1, 2, 1 0 0 0…”