### Table 2: A sample data set illustrates clusters embedded in subspaces of a high dimensional space.

2003

"... In PAGE 2: ... Hence, a good subspace clustering algorithm should be able to find clusters and the maximum associated set of dimensions. Consider, for example, a data set with 5 data points of 6 dimensional(given in Table2 ). In this data set, it is obvious that C = {x1, x2, x3} is a cluster and the maximum set of dimensions should be P = {1, 2, 3, 4}.... In PAGE 3: ...here sj is a vector defined as sj = (Aj1, Aj2, ..., Ajnj)T. Since there are possibly multiple states(or values) for a vari- able, a symbol table of a data set is usually not unique. For example, for the data set in Table2 , Table 3 is one of its symbol tables. BC BS A A A A B B B B C C C C D D D D BD BT Table 3: One of the symbol tables of the data set in Table 2.... In PAGE 3: ... For a given symbol table of the data set, the frequency table of each cluster is unique according to that symbol table. For example, for the data set in Table2 , let (C, P) be a subspace cluster, where C = {x1, x2, x3} and P = {1, 2, 3, 4}, if we use the symbol table presented in Table 3, then the corre- sponding frequency table for the subspace cluster (C, P) is given in Table 4. From the definition of frequency fjr in Equation (6), we have the following equalities: nj CG r=1 fjr(C) = |C|, j = 1, 2, .... ..."

Cited by 4

### Table 3: Comparative tests on synthetic data. data # clusters/ correct clusters/subspaces found by set subspaces CLIQUE RIS E SURFING

2004

"... In PAGE 8: ... Like CLIQUE, in some cases RIS failed to detect the correct subspaces due to the utiliza- tion of a global density parameter (cf. Table3 ). For example, applied to data set 16, RIS was able to com- pute the lower dimensional subspaces, but could not detect the higher dimensional one.... ..."

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### Table 4: Throughput and jitter in UAV over A/V Streams

2000

"... In PAGE 8: ...3 ms, meaning the pipeline had completely adequate throughput for the test video (data rate 1 Mbit per second). In addition, in the A/V Streams implementation we ob- served several peaks or clusters of interframe times whose modal values are recorded in Table4 The maximum interframe... ..."

### Table 2: FDC results. The consensus clustering is as good as or better than the best individual subspace clustering.

2002

"... In PAGE 5: ... We also conducted experiments on the other four data- sets. Table2 summarizes the results. The choice of the number of random subspaces D6 and their dimensionality is currently driven by the user.... ..."

Cited by 144

### Table 1: Results on synthetic data sets. data d cluster N # subspaces time

2004

"... In PAGE 6: ...1. Efficiency The runtimes of SURFING applied to the synthetic data sets are summarized in Table1 . In all experiments, we set k = 10.... In PAGE 6: ... In most cases, this ratio is even significantly less than 1%. For data set 10 in Table1 where the cluster is hidden in a 12-dimensional subspace of a 15- dimensional feature space, SURFING only computes 12.5% of the possible subspaces.... In PAGE 6: ...2.5% of the possible subspaces. Finally, for both the real world data sets, SURFING computes even signif- icantly less than 0.1% of the possible subspaces (not shown in Table1 ). The worst ever observed percentage was around 20%.... ..."

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### Table 3: FDC results. The consensus clustering is as good as or better than the best individual subspace clustering.

2002

"... In PAGE 18: ... We also conducted FDC experiments on the other three data-sets. Table3 summarizes the results and several comparison benchmarks. The choice of the number of random... ..."

Cited by 144

### Table 3: FDC results. The consensus clustering is as good as or better than the best individual subspace clustering.

2002

"... In PAGE 18: ... We also conducted FDC experiments on the other three data-sets. Table3 summarizes the results and several comparison benchmarks. The choice of the number of random... ..."

Cited by 144

### Table 2: FDC results. The consensus clustering is as good as or better than the best individual subspace clustering.

"... In PAGE 5: ... We also conducted experiments on the other four data- sets. Table2 summarizes the results. The choice of the number of random subspaces r and their dimensionality is currently driven by the user.... ..."

### Table 10: Sets of subspace dimensions associated with each cluster for the congressional voting data, where Q = {1, 2, ..., 16}.

2003

"... In PAGE 7: ...1, 2, ..., 16}. The subspace dimensions associated with the clusters of con- gressional voting data is given in Table10 . Similar to the clustering results of Wisconsin breast cancer data, one clus- ter of the congressional voting data has a low dimensionality while another has a high dimensionality.... ..."

Cited by 4

### Table 6.1: Requirements for incremental data stream analysis algorithms [43].

2005