### Table 4. Comparison of the outliers detected by explicit and rule-based detection approaches for differently defined patient record classes.

in 5th International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2000)

"... In PAGE 19: ... The rule de- tected positive patient records 214 and 230 as outliers within the test set. The results are presented in the first row of Table4 . Comparing results obtained by explicit and rule-based outlier detection it can be noted that the later approach selected the same records as the former approach but that it also detected two more records: 165 and 185.... In PAGE 20: ... The limit values between positive and negative classes are based on induced rules in previous experiments. Table4 in its second and third row includes results obtained for positive classes defined by con- ditions a75a23a76 a75a23a77a23a78a23a79 a80a48a81a83a82 a84 a85 a86 a87a15a87 and a88a48a89a23a90a50a79 a80a48a81a83a82 a84 a91 a86 a87a15a87 respec- tively. In the left column are records detected as outliers in the test set by explicit approach while in the right one are detected by the rule-based method.... In PAGE 27: ...9 95.1 Table4 shows the prediction accuracies of the two methods. Re- duced data set A2, from which the multivariate outliers and the examples with the most univariate outlier values were removed, was the worst nearest neighbour classifier.... In PAGE 53: ... From these subsets Decision Master induced decision trees and calculated the error rate based on cross validation. We observed that a better error rate can be reached if the decision tree is only induced from a subset of features, see Table4 and Figure 4. The method used in this paper does not tell us what is the right number of features.... In PAGE 53: ... Another side effect is that the resulting decision tree is more compact, see Figure 3. Feature Number Unprunend Decision Tree Error Rate pruned Decision Tree Error Rate 19 6,8571 7,428 10 10,85 14,85 13 7,4286 4,5714 15 7,429 4,5714 17 10,28 7,42 Table4... ..."

### Table 2 Outlier detection performances on the UCI datasetsa

1999

"... In PAGE 6: ... To estimate the errors (of the first and the second kind) n-fold cross-validation with n 5 is used. In Table2 , the performances of the outlier de- tection methods on all UCI datasets are shown. For each method, the performance on a target validation set (left) and an outlier test set (right) is shown.... ..."

Cited by 59

### Table 6: Complexity of operations on decision diagrams.

2002

"... In PAGE 37: ... It is well known that using a different branching factor, or representing a function by a vector of diagrams, has no effect on the complexity of the operations used during model-checking [41]. The middle column of Table6 summarizes these complexities of the operations from the left column with respect to the size of the graph representing the diagram. Note that even though we can think of representing an mv-set using a vector of diagrams, the underlying implementation constructs a single directed acyclic graph.... In PAGE 37: ... Moreover, since the underlying graph is connected, we can express the complexity of operations relative to the number of nodes in this graph. These complexities are given in the right column of Table6 , where a9 is the number of nodes and a33 is the branching factor of the decision diagram. Using this representation of complexity, we infer the expected running time based on the empirical evidence on the sizes of different decision diagrams.... ..."

### Table 1: Complexity results for outlier detection

"... In PAGE 6: ... We have formally de- ned the notion of an outlier and an outlier witness, and analyzed the complexities involved, pointing out some non- trivial tractable subsets. The complexity results are summa- rized in Table1 , where a102 -c stands for a102 -complete. As ex- plained in the introduction, outlier detection can also be used for maintaining database integrity and completeness.... ..."

### Table 2. Comparison of the outlier detection methods

"... In PAGE 10: ...Observe that the M estimator based robust regression approach does not identify outliers. Table2 provides values of the three measures used to assess the methods as well as the regression equation parameter values computed in the way explained above. The obtained results show that the multilayer per- cpetron is the best technique for categorizing the data, followed by the PCA based approach.... ..."

### Table 2: Outlier Detection Application Comparisons

2006

"... In PAGE 8: ...00) and data set were used for all the experiments. Results : Table2 (a) compares the various versions of the OD application for their execution cost, code size and memory requirements. It lists the applications in decreas- ing proportion of their functionality implemented in native code.... ..."

Cited by 4

### Table 3. Comparison of conventional and robust regression by using hypothetical data and data with two outliers. Detected outliers are italicised

"... In PAGE 2: ...02. For the data with two outliers in Table3 , al is 1.10.... ..."

### Table 1. Comparison of conventional and robust regression methods using hypothetical data and data with one outlier. Detected outliers are italicised

"... In PAGE 1: ...90 + 1.69~ and the calibration results are given in Table1 . The residuals listed show that the LS line is attracted strongly by this single outlier and therefere fits the Table 1.... In PAGE 2: ... 2. Illustration of SM for the data in Table1 . The value of a1 versus the ranks of the slopes of all pairs of points; 4-5 indicates the slope of the line between the fourth and the fifth data point, etc.... In PAGE 3: ... The breakdown point is therefore 50%. To illustrate the method the data in Table1 are again used. Firstly, the slopes of each of the cn2 combinations of pairs of points are calculated, then the squared residuals towards the line for each measuring point are calculated, the resulting squared residuals are sorted and their medians obtained.... In PAGE 3: ... 3. Illustration of RM for the data in Table1 . (a) Ranked slope al for each point i, joined by a line to each of the other points; and (b) ranked median slopes selected from (a).... In PAGE 3: ... 4. Illustration of LMS for the data in Table1 . The ranked log of the median of squared residuals for the lines through the different ... In PAGE 6: ... Such an example is shown in Fig. 9, where the objective function (or rather its inverse for graphical purposes) is given for the data of Table1 as a function of a. and al.... ..."

### Table 3.1: Description of the data sets used for the outlier detection exper- iments. #Outliers is the number of observations affected.

2006

### Table 4. Directory-based outlier detection results.

"... In PAGE 9: ...2. Case Study Results The / and /usr/ partitions from the Forensic Chal- lenge were analyzed using the directory outlier analysis script and the results can be found in Table4 . There is only one directory in this system that contains hidden files and it is the /usr/man/.... ..."