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High-dimensional data is often very sparse, e.g. most entries in the data matrix are zero. Using this sparsity in efficient data structures and algorithms can reduce temporal and storage complexity by several orders of magnitude. Scalability can be investigated in terms of the number of objects $ n$ and the number of dimensions $ d$. Traditionally, scaling to large $ n$ has been considered more. In this dissertation, we focus on applications with large $ d$. For scaling to large $ n$, there are four main ways of reducing complexity in order to scale clustering algorithms:

Sample the data, cluster the sample points and then use a quick heuristic to allocate the non-sampled points to the initial clusters. This approach will yield a faster algorithm at the cost of some possible loss in quality, and is employed, for example in the Buckshot algorithm for the Scatter/Gather approach to iterative clustering for interactive browsing [CKPT92]. If the sample is $ O(\sqrt n)$, and `nearest cluster center' is used to allocate the remaining points, one obtains an $ O(kn)$ algorithm. Also related are randomized approaches that can partition a set of points into two clusters of comparable size in sublinear time, producing a (1+$ \epsilon$) solution with high probability [Ind99]. We shall show later that since OPOSSUM is based on balanced clusters, sampling is a good choice since one can ensure with high probability that each cluster is represented in the sample without needing a large sample size.
Sequential building.
Construct a `core' clustering using a small number of elements, and then sequentially scan the data to allocate the remaining inputs, creating new clusters (and optionally adjusting existing centers) as needed. Such an approach is seen e.g., in BIRCH [ZRL97]. This style compromises balancing to some extent, and the threshold determining when a new cluster is formed has to be experimented with to bring the number of clusters obtained to the desired range. A version of this approach for graph partitioning using a corrupted clique model was proposed by [BDSY99] and applied to clustering gene expressions. This can be readily used for OPOSSUM as well. Sequential building is specially popular for out-of-core methods, the idea being to scan the database once to form a summarized model (for instance, the size, sum and sum-squared values of each cluster [BFR98]) in main memory. Subsequent refinement based on summarized information is then restricted to main memory operations without resorting to further disk scans.
Compare with representatives rather than with all points. Using $ m < n$ representatives reduces the number of similarities to be considered from $ O(n^2)$ to $ O(nm)$. For example, in $ k$-means, the current cluster means are used as representatives. Since points do not have to compared to all others but only to a few centroids (the current means), scalability is considerably improved. The results, however, become sensitive to the initial selection of representatives. Also, representatives might have to be updated resulting in an iterative algorithm.
Apply prior domain knowledge to pre-segment the data, e.g. using indices or other `partitionings' of the input space. Pre-segmentations can be coarser (e.g., to reduce pairwise comparisons by only comparing within segments) or finer (e.g., to summarize points as a pre-processing step as in BIRCH) than the final clustering. As mentioned earlier, this becomes increasingly problematic as the dimensionality of the input space increases to the hundreds or beyond, where the suitable segments may be difficult to estimate, pre-determine, or populate.
All these approaches are somewhat orthogonal to the main clustering routine in that they can be applied in conjunction with most core clustering routines to save computation, at the cost of some loss in quality.

next up previous contents
Next: Visualization Up: Background and Related Work Previous: Graph and Hypergraph Partitioning   Contents
Alexander Strehl 2002-05-03