### Table 2. 4. The set of sequential patterns is the collection of patterns found in the above recursive mining

2004

"... In PAGE 7: ... The subsets of sequential patterns can be mined by construct- ing the corresponding set of projected databases and mining each recursively. The projected databases as well as sequential patterns found in them are listed in Table2 , while the mining process is explained as follows: a. Find sequential patterns with prefix hai.... ..."

Cited by 31

### Table 12. Implemented data mining and statistical methods

2003

"... In PAGE 16: ... Data Mining and Statistics. Table12 shows the data mining and statistical methods currently implemented in the different databases. We can observe that GeneX, SMD and M-CHIPS offer the most comprehensive facilities for data mining, allowing the user to perform various clustering methods, such as the hierarchical and K-means algorithms.... ..."

Cited by 4

### Table 1 Classification of data mining and text data mining approaches by Hearst (1999:5)

in Contents

"... In PAGE 6: ... apos; Before a critical discussion of Hearst apos;s paper can be given, it is necessary to come to a clear understanding of her views. Therefore, her paper has been summarized by expanding her own table ( Table1 ) to include other information and judgements in the paper (Table 2). 4 Clarifiying Hearst Hearst apos;s paper is innovative and groundbreaking because it distinguishes between different types of data mining and text mining ( vs database queries and information retrieval).... ..."

### Table 2 lists some notations used throughout this paper. In the context of mining quantitative databases, we have p(vx) = supp(x[vx, vx]) and p(vx, vy) = supp(x[vx, vx]y[vy, vy]).

2006

"... In PAGE 2: ... Entropy measures the un- certainty of a random variable, while MI describes how much information one random variable tells about another one. Table2 : Notations... ..."

Cited by 4

### Table 2. Mining measures w.r.t r and tr

"... In PAGE 8: ... 4.1 Mining in the transposed database We detail in Table2 di erent measures about mining in both datasets, with- out or with transposing, with the discretization method described above (more complete results with other discretization methods can be found in [RRB+03, BRC+04]). We give the number of candidates to the frequency test in the database, and those which passed it (free patterns).... ..."

### Table 2.2.1a: Distribution, Type, and Amount of Alkaline Materials Used (Appendix A, EPA Remining Database, 1999)

### Table 3. Phases 1 to 4 of Table 2 using MINE RULE.

1998

"... In PAGE 5: ...asks. It is not possible here to consider all the aspects of such an operator. We introduce it by means of one typical example and refer to [14] for other examples and a complete de nition of its syntax and operational semantics. Given the dataset r 1 as de ned in Table 2, phase 4 is de ned bytheMINE RULE statementin Table3 . The MINE RULE operator takes a relational database and produces an SQL3 table [10] in which each tuple denotes a mined rule.... ..."

Cited by 7

### Table 1.1: Relation CityLocation in the database CityData Suppose that a data mining task is to predict potential value for the attribute unemployment pct, for a city located in California, based on a set of attributes: fam- ily income, poverty pct, crime rate, and bachelor pct. The task can be written in a data mining query language, DMQL [23], as below. use CityData

### Table 6. Although HEP is slower than logistic regres- sion, TAN, and NB, it is much faster than MDLEP. More- over, HEP is able to learn Bayesian networks from a large database in one minute. Thus, it can be used in real-life data mining applications.

in Decision

"... In PAGE 8: ... Table6 . The execution time for different meth- ods.... ..."

### Table 1: Database properties

1997

"... In PAGE 8: ... The data-mining provides information about the set of items generally bought together. Table1 shows the databases used and their properties. The numberof transactions is denoted as jDj, average transaction size as jTj, and the average maximal potentially frequent itemset size as jIj.... ..."

Cited by 24