### Table 3 Cluster accuracy and stability on the completely synthetic data with four repeated measurements at high noise level

2003

"... In PAGE 7: ...cluster quality over the approach of averaging over repeated measurements using the same algorithms at high noise level. In terms of cluster stability (see Table3 b), the following three approaches yield average adjusted Rand index above 0.900: the elliptical model of the IMM approach; the comment reviews reports deposited research interactions information refereed research http://genomebiology.... ..."

### Table 1. Results of clustering (* clustering runs that exclude data from one array)

2004

"... In PAGE 9: ...Using K-Means clustering with a pre-selected number (K) of clusters, we identified the most significant up- and down-regulated genes common to all or most arrays. Table1 shows the number of identified genes in each of the 6 clustering runs. Runs 1 to 3 include data from all 7 replicate arrays, and runs 4 to 6 exclude data from the abnormal array.... In PAGE 9: ... 4). The numbers of genes identified through six runs of clustering, with or without this abnormal array were different ( Table1 ). This demonstrates the influences of including or excluding abnormal data on the knowledge discovery process.... ..."

### Table 1: Mapping LDA to Source Code

"... In PAGE 3: ... Thus a source code file can be thought of as a mixture of these domain topics. Applying LDA to the source code now reduces to map- ping source code entities of a software system to the LDA model, described in Table1 . Given this mapping, applica- LDA Model Source Code Entities word We define domain specific keywords ex- tracted from names of program elements such as functions, files, data structures and... ..."

### Table 1: Topics and associated data. Ad hoc retrieval topics, number of relevant documents, and average results for all runs.

2006

"... In PAGE 7: ... Results A total of 27 research groups submitted 47 different runs. Table1 shows the pool size, number of relevant docu- ments, mean average precision (MAP), average precision at 10 documents, and average precision at 100 documents for each topic. (Precision at 100 documents is potentially compromised due to a number of topics having many fewer than 100 relevant documents and thus being unable to score well with this measure no matter how effective they were at ranking relevant documents at the topic of list.... In PAGE 7: ... (Precision at 100 documents is potentially compromised due to a number of topics having many fewer than 100 relevant documents and thus being unable to score well with this measure no matter how effective they were at ranking relevant documents at the topic of list. However, as noted in Table1 , the mean and median number of relevant documents for all topics was over 100 and, as such, all runs would be affected by this issue.) The results of the duplicate judgments for the kappa sta- tistic are shown in Table 2.... In PAGE 8: ... Figure 2 shows the official results graph- ically with annotations for the first run statistically signif- icant from the top run as well as the OHSU quot;baseline. quot; As typically occurs in TREC ad hoc runs, there was a great deal of variation within individual topics, as is seen in Table1 . Figure 3 shows the average MAP across groups for each topic.... ..."

### Table 2: Average precision per topic, for Textual runs, BoB runs and combined runs

2003

"... In PAGE 7: ...boat water sky Find additional shots with one or more US flags flap- ping Find shots with one or more sailboats, sailing ships, clipper ships, or tall ships - with some sail(s) unfurled a b Figure 2: Selecting components from images textual part we tried both short and long queries, for the visual part we used full queries and best-example queries. Table2 shows the results for combinations with the BoB measure. We also experimented with combinations with the ALA measure, but we found that in the ALA case it is difficult to combine textual and visual scores, because they are on different scales (see also Appendix A).... ..."

Cited by 7

### TABLE I. Measuring Ruggedness of Stability and Folding Rate Maps

Cited by 3

### Table 1: The stability of the diametrical clustering algorithm is indi- cated by the low standard deviation of the total squared correlation coef cient for clusters produced by Phase I of the algorithm.

2003

"... In PAGE 5: ... Stability: To measure the stability of the algorithm we computed the standard deviation of the total squared corre- lation coef cient (see ( 3)) over 20 runs. Table1 shows that the standard deviation of the squared correlation coef cient is small compared to its mean value and hence our algorithm is quite stable. The standard deviations of HAve and SAve (de ned later) values (not shown here) are also small on all... ..."

Cited by 11

### Table 2: The micro-averaged accuracy results of three clustering methods were pre- sented after 10 different runs on the heart disease data set. In this experiment, the optimal number of clusters which partitions the Heart Disease data set into subsets disjointly is 5. The average-link of hierarchical clustering is used.

2004

"... In PAGE 12: ... By staring up from the better-than-average ground, it is highly likely for an iterative clustering to find the global optimum. Table2 shows a result from comparison of three clustering methods after carrying out 10 different runs on the heart disease data set. The performance of iterative methods measured in micro-averaged accuracy are fluctuated between 50% and 80%, whereas those of hierarchical method showed a stabilized accuracy at 80%.... ..."

Cited by 1

### Table 1: LDA results of two di erent \Jun Yang quot;.

"... In PAGE 2: ...Table 1: LDA results of two di erent \Jun Yang quot;. Table1 lists an illustrative result from LDA. We depict topics that clearly show the di erences for disambiguating authors with exactly the same name.... ..."

### Table 1 Clustering at the first level

2000

"... In PAGE 11: ... The optimal cluster number is achieved automatically using the introduced method. The clustering results at the first level are shown in Table1 , based upon the calculation of cluster concentration levels, level decrease measures, and relative decrease measures considering different cluster numbers using Eqs. (8) and (9).... In PAGE 11: ... (8) and (9). From Table1 , the optimal cluster number with the minimum value of relative decrease measure is iden- tified as 3. So, the delivery tasks are classified into three clusters at the first level, namely C1, C2, and C3.... ..."