"... In PAGE 8: ... Each group is then converted to a partial order. In Table2 the properties of the described approaches for pattern discovery in interval series and sequences are listed in order of increasing expressivity of the pattern language and earlier publication. All except the second method work on multivariate interval series and interval sequences.... ..."

"... In PAGE 18: ... Thus, the p-projected database becomes {fcam, cb, fcam}. This is very similar to the the p-conditional pattern- base shown in Table2 except fcam and fcam are expressed as (fcam:2) in Table 2. After that, the p-conditional FP-tree can be built on the p-projected database based on the FP-tree construction algorithm.... ..."

"... In PAGE 3: ... A possible way to find such keywords or phrases is to find the keywords that frequently appear in the target set as in traditional data mining. However, this does not works in most text collections because in a typical English text, the most frequent keywords are stopwords like the or an (see Table1 (a) and Table 3 (a)). These keywords are basic constituents of English grammars and convey no information on the contents of the text collection.... In PAGE 3: ... We can easily observe that most stopwords appear evenly in the target and the control set, while informative keywords appear more frequently in the target set than the control set. Therefore, the optimized pattern discovery algorithm will find those keywords or phrases that appear more frequently in the target set than the control set by minimizing a given statistical measure such as the information entropy or the prediction error (See Table1 (b)-(c) and Table 3 (b)-... In PAGE 6: ...2.2 The first experiment In Table1 , we show the list of the phrase patterns discovered by our mining system, which capture the category ship relative to other categories of Reuters newswires. In Fig.... In PAGE 6: ...ontain the topic keywords in the major news stories for the period in 1987 (Fig. 1(b)). Such keywords are hard to find by traditional frequent pattern discovery because of the existence of the high frequency words such as CWtheCX and CWareCX. The patterns of medium rank (BEBIBD AO BEBJBC) are long phrases, such that CWlloyds shipping intelligenceCX and CWiranian oil platformCX, as a summary ( Table1 (c)), which cannot be represented by any combination of non-contiguous keywords. 3.... ..."

"... In PAGE 2: ... A possible way to find such keywords or phrases is to find the keywords that frequently appear in the target set as in traditional data mining. However, this does not works in most text collections because in a typical English text, the most frequent keywords are stopwords like the or an (see Table1 (a) and Table 3 (a)). These keywords are basic constituents of English grammars and convey no informa- tion on the contents of the text collection.... In PAGE 2: ... We can easily observe that most stopwords appear evenly in the target and the con- trol set, while informative keywords appear more frequently in the target set than the control set. Therefore, the opti- mized pattern discovery algorithm will find those keywords or phrases that appear more frequently in the target set than the control set by minimizing a given statistical measure such as the information entropy or the prediction error (See Table1 (b)-(c) and Table 3 (b)-(c)). 2.... In PAGE 4: ...2. The first experiment In Table1 , we show the list of the phrase patterns dis- covered by our mining system, which capture the category ship relative to other categories of Reuters newswires. In Fig.... ..."

"... In PAGE 40: ... Therefore, we dug into deeper contexts to mine double-term and triple-term frequent patterns. Table8 presents the top-ranked extracted patterns. It is apparent that the double-term patterns in both lists are still quite similar: 3 out of 5 most frequent patterns are the same for positive and negative reviews.... ..."

"... In PAGE 5: ... Assume that the minimum support is set to 0.5, we can get the set of all frequent patterns as shown in Table2 . According to formula (1), we can get the outlier factor value of each record as shown in Table 1 (the fifth column).... ..."

"... In PAGE 10: ...hreshold is needed to mine a significant number of patterns. E.g. though there are 15426 pairwise non-isomorphic cycles in the database, only a few of them are really frequent; the only one above 10% is the benzene ring with frequency 66%. Our results are given in Table1 . It shows the number of candidate (#C) and frequent (#FP) k-patterns discovered for k = 1, .... ..."

"... In PAGE 10: ...hreshold is needed to mine a significant number of patterns. E.g. though there are 15426 pairwise non-isomorphic cycles in the database, only a few of them are really frequent; the only one above 10% is the benzene ring with frequency 66%. Our results are given in Table1 . It shows the number of candidate (#C) and frequent (#FP) k-patterns discovered for k = 1, .... ..."

"... In PAGE 5: ... 3.2 Frequent pattern discovery The FPs around the catalytic residues in the two serine pro- tease subfamilies are found to be quite different, as listed in Table3 . The different FPs observed for the same residues in the two families suggest that the microenvironment has a strong effect on the specific catalytic function.... ..."