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Object-Distributed Clustering (ODC)

A dual to the application described in the previous section, is Object-Distributed Clustering (ODC). In this scenario, individual clusterers have a limited selection of the object population but have access to all the features of the objects it is provided with.

This is somewhat more difficult than FDC, since the labelings are partial. Because there is no access to the original features, the combiner $ \Gamma$ needs some overlap between labelings to establish a meaningful consensus5.11. Object-distribution can naturally result from operational constraints in many application scenarios. For example, datamarts of individual stores of a retail company may only have records of visitors to that store, but there are enough people who visit more than one store of that company to result in the desired overlap. On the other hand, even if all the data is centralized, one may artificially `distribute' them in the sense of running clustering algorithms on different but overlapping samples (record-wise partitions) of the data, and then combine the results as this can provide a computational speedup when the individual clusterers have super-linear time complexity.

In this subsection, we will discuss how one can use consensus functions on overlapping sub-samples. We propose a wrapper to any clustering algorithm that simulates a scenario with distributed objects and a combiner that does not have access to the original features. In ODC, we introduce an object partitioning and a corresponding clustering merging step. The actual clustering is referred to as inner loop clustering. In the pre-clustering partitioning step $ \Pi$, the entire set of objects $ \mathcal{X}$ is decomposed into $ p$ overlapping partitions $ \pi$:

$\displaystyle \mathcal{X} \stackrel{\Pi}{\rightarrow} \{\pi^{(q)} \left\vert \right. q \in \{1,\ldots,p\} \}$ (5.7)

Two partitions $ \pi^{(a)}$ and $ \pi^{(b)}$ are overlapping iff $ \vert \pi^{(a)} \cap \pi^{(b)} \vert > 0$.5.12Also, a set of partitions provides full coverage iff $ \mathcal{X} = \bigcup_{q=1}^{p} \pi^{(q)}$.

The ODC framework is parametrized by $ p$, the number of partitions, and $ l$, the degree of overlap ranging between 0 and 1. No overlap, or $ l=0$, yields disjoint partitions and, by design, $ l=1$ implies $ p$-fold fully overlapping (identical) samples. Instead of $ l$, one can use the parameter $ v>1$ to fix the total number of points processed in all $ p$ partitions combined to be (approximately) $ vn$. This is accomplished by choosing $ l = (v-1) /

Let us assume that the data is not ordered, so any contiguous indexed subsample is equivalent to a random subsample. Every partition should have the same number of objects for load balancing. Thus, in any partition $ \pi$ there are $ \vert\pi\vert = \lceil
\frac{p-1}{p} nl \rceil + \lceil \frac{n}{p} \rceil$ objects. Now that we have the number of objects $ \vert\pi\vert$ in each partition, let us propose a simple coordinated sampling strategy: For each partition there are $ \lceil n / p \rceil$ objects deterministically picked so that the union of all $ p$ partitions provides full coverage of all $ n$ objects. The remaining objects for a particular partition are then picked randomly without replacement from the objects not yet in that partition. There are many other ways of coordinated sampling. In this chapter we will limit our discussion to this one strategy for brevity.

Each partition is processed by independent, identical clusterers (chosen appropriately for the application domain). For simplicity, we use the same $ k$ in the sub-partitions. The post-clustering merging step is done using our supra-consensus function $ \Gamma$.

$\displaystyle \{\mathbf{\lambda}^{(q)} \left\vert \right. q \in \{1,\ldots,p\} \} \stackrel{\Gamma}{\rightarrow} \mathbf{\lambda}$ (5.8)

Since every partition only looks at a fraction of the data, there are missing labels in the $ \mathbf{\lambda}^{(q)}$'s. Given sufficient overlap, the supra-consensus function $ \Gamma$ ties the individual clusters together and delivers a consensus clustering.

We performed the following experiment to demonstrate how the ODC framework can be used to perform clustering on partially overlapping samples without access to the original features. We use graph partitioning as the clusterer in each processor and vary the number of partitions from 2 to 72 with $ v = 2$. Figure 5.11 shows our results for the four data-sets. Each plot in figure 5.11 shows the relative mutual information (fraction of mutual information retained as compared to the reference clustering on all objects and features) as a function of the number of partitions. We fix the sum of the number of objects in all partitions to be double the number of objects ($ v = 2$). Within each plot, $ p$ ranges from 2 to 72 and each ODC result is marked with a `$ \circ$'. The reference point for unpartitioned data is marked by `$ \times$' at (1,1). For each of the plots, we fitted a sigmoid function to summarize the behavior of ODC for that scenario.

Clearly, there is a tradeoff in the number of partitions versus quality. As $ p$ approaches $ vn$, each clusterer only receives a single point and can make no reasonable grouping. For example, in the YAHOO case, for $ v = 2$ processing on 16 partitions still retains around 90% (80%) of the full quality. For less complex data-sets, such as 2D2K, combining 16 partial partitionings ($ v = 2$) still yields 90% of the quality. In fact, 2D2K can be clustered in 72 partitions at 80% quality. Also, we observed that for easier data-sets there is a smaller absolute loss in quality for more partitions $ p$.

Regarding our proposed techniques, all three algorithms achieved similar ANMI scores without significant differences for 8D5K, PENDIG, and YAHOO. HGPA had some instabilities for the 2D2K data-set delivering inferior consensus clusterings compared to MCLA and CSPA.

In general, we believe the loss in quality with $ p$ has two main causes. First, through the reduction of considered pairwise relations, the problem is simplified as speedup increases. At some point too much relationship information is lost to reconstruct the original clusters. The second factor is related to the balancing constraints used by the graph partitioner in the inner loop: the sampling strategies cannot maintain the balancing, so enforcing them in clustering hurts quality. A relaxed inner loop clusterer might improve results.

Figure 5.11: Object-Distributed Clustering (ODC) results. Clustering quality (measured by relative mutual information) as a function of the number of partitions, $ p$, on various data-sets: (a) 2D2K; (b) 8D5K; (c) PENDIG; (d) YAHOO. The sum of the number of samples over all partitions is fixed at $ 2n$. Each plot contains experimental results using graph partitioning in the inner loop for $ p =
\left[2,\ldots,72\right]$ and a fitted sigmoid (least squared error). Processing time can be reduced by a factor of 4 for the YAHOO data while preserving 80% of the quality ($ p=16$).
\resizebox{.45\textwidth}{!}{\includegraphics*{eps/2dga-qvp-mi}} \resizebox{.45\textwidth}{!}{\includegraphics*{eps/8dga-qvp-mi}}
(a) (b)
\resizebox{.45\textwidth}{!}{\includegraphics*{eps/pend-qvp-mi}} \resizebox{.45\textwidth}{!}{\includegraphics*{eps/ml-pddp-qvp-mi}}
(c) (d)

Distributed clustering using a cluster ensemble is particularly useful when the inner loop clustering algorithm has superlinear complexity ($ >O(n)$) and a fast consensus function (such as MCLA and HGPA) is used. In this case, additional speedups can be obtained through distribution of objects. Let us assume that the inner loop clusterer has a complexity of $ O(n^2)$ (e.g., similarity-based approaches or efficient agglomerative clustering) and one uses only MCLA and HGPA in the supra-consensus function.5.13We define speedup as the computation time for the full clustering divided by the time when using the ODC approach. The overhead for the MCLA and HGPA consensus functions grows linear in $ n$ and is negligible compared to the $ O(n^2)$ clustering. Hence the sequential speedup is approximately $ s^{(\mathrm{ODC-SEQ})} =
\frac{p}{v^2}$. Each partition can be clustered without any communication on a separate processor. At integration time only the $ n$-dimensional label vector (instead of e.g., the entire $ n \times n$ similarity matrix) has to be transmitted to the combiner. Hence, ODC does not only save computation time, but also enables trivial $ p$-fold parallelization. Consequently the sequential speedup can be multiplied by $ p$ if a $ p$-processor computer is utilized: $ s^{(\mathrm{ODC-PAR})} =
\frac{p^2}{v^2}$. An actual speedup ($ s > 1$) will be reached for $ p > v^2$ in the sequential or $ p > v$ in the parallel case. For example, when $ p=4$ and $ v = 2$ (which implies $ l=1/3$) the computing time is approximately the same ($ s=1$) because each partition is half the original size $ n/2$ and consequently processed in a quarter of the time. Since there are four partitions, ODC takes the same time as the original processing. In our experiments, using partitions from 2 to 72 yield corresponding sequential (parallel) speedups from 0.5 (1) to 18 (1296). For example, 2D2K (YAHOO) can be sped up 64-fold using 16 processors at 90% (80%) of the full length quality. In fact, 2D2K can be clustered in less that 1/1000 of the original time at 80% quality.

next up previous contents
Next: Summary Up: Cluster Ensemble Applications and Previous: Feature-Distributed Clustering (FDC)   Contents
Alexander Strehl 2002-05-03