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36
Levelwise Search and Borders of Theories in Knowledge Discovery
, 1997
"... One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm fo ..."
Abstract

Cited by 263 (15 self)
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One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm
Levelwise Search of Frequent Patterns with Counting Inference
, 2000
"... In this paper, we propose the algorithm Pascal which introduces a novel optimization of the wellknown algorithm Apriori. Being provided with a given minsup threshold, Pascal discovers all frequent patterns by performing as few counting as possible. In order to derive the support of larger patterns ..."
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In this paper, we propose the algorithm Pascal which introduces a novel optimization of the wellknown algorithm Apriori. Being provided with a given minsup threshold, Pascal discovers all frequent patterns by performing as few counting as possible. In order to derive the support of larger patterns without accessing the database whenever it is possible, we use the knowledge about the support of some of their subpatterns, the socalled key patterns. Experiments comparing Pascal to the three algorithms Apriori, Close and MaxMiner, each of which being representative of a frequent patterns discovery strategy, show that Pascal is the most efficient algorithm for extracting patterns from strongly correlated data. Moreover, its execution times are equivalent to those of Apriori and MaxMiner when data is weakly correlated.
A Declarative Language Bias for Levelwise Search of FirstOrder Regularities
, 1998
"... . The paper presents a hypothesis language declaration formalism and a corresponding refinement operator that successfully combines the levelwise search principle with a firstorder hypothesis language and thus provides an improvement to the socalled optimal refinement operators that are commonly u ..."
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Cited by 3 (0 self)
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. The paper presents a hypothesis language declaration formalism and a corresponding refinement operator that successfully combines the levelwise search principle with a firstorder hypothesis language and thus provides an improvement to the socalled optimal refinement operators that are commonly
Levelwise Construction of Decision Trees for Classification
"... A partitionbased framework is presented for a formal study of classification problems. An information table is used as a knowledge representation, in which all basic notions are precisely defined by using a language known as the decision logic language. Solutions to, and solution space of, classifi ..."
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Cited by 1 (0 self)
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, classification problems are formulated in terms of partitions. Algorithms for finding solutions are modelled as searching in a space of partitions under the refinement order relation. We focus on a particular type of solutions called conjunctively definable partitions. Two levelwise methods for decision tree
ExAMiner: Optimized Levelwise Frequent Pattern Mining with Monotone Constraints
 In ICDM
"... The key point of this paper is that, in frequent pattern mining, the most appropriate way of exploiting monotone constraints in conjunction with frequency is to use them in order to reduce the problem input together with the search space. Following this intuition, we introduce ExAMiner, a levelwise ..."
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Cited by 23 (7 self)
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The key point of this paper is that, in frequent pattern mining, the most appropriate way of exploiting monotone constraints in conjunction with frequency is to use them in order to reduce the problem input together with the search space. Following this intuition, we introduce ExAMiner, a levelwise
LEEWAVE: LevelWise Distribution of Wavelet Coefficients for Processing kNN Queries over Distributed Streams
"... We present LEEWAVE − a bandwidthefficient approach to searching rangespecified knearest neighbors among distributed streams by LEvElwise distribution of WAVElet coefficients. To find the k most similar streams to a rangespecified reference one, the relevant wavelet coefficients of the reference ..."
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We present LEEWAVE − a bandwidthefficient approach to searching rangespecified knearest neighbors among distributed streams by LEvElwise distribution of WAVElet coefficients. To find the k most similar streams to a rangespecified reference one, the relevant wavelet coefficients
On Monotone Data Mining Languages
 In Proc. of International Workshop on Database Programming Languages (DBPL
, 2001
"... We present a simple Data Mining Logic (DML) that can express common data mining tasks, like "Find Boolean association rules" or "Find inclusion dependencies." At the center of the paper is the problem of characterizing DML queries that are amenable to the levelwise search strateg ..."
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Cited by 7 (2 self)
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We present a simple Data Mining Logic (DML) that can express common data mining tasks, like "Find Boolean association rules" or "Find inclusion dependencies." At the center of the paper is the problem of characterizing DML queries that are amenable to the levelwise search
Towards a Text Mining Methodology Using Frequent Itemsets and Association Rule Extraction
"... Abstract: This paper proposes a methodology for text mining relying on the classical knowledge discovery loop, with a number of adaptations. First, texts are indexed and prepared to be processed by frequent itemset levelwise search. Association rules are then extracted and interpreted, with respect ..."
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Cited by 1 (1 self)
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Abstract: This paper proposes a methodology for text mining relying on the classical knowledge discovery loop, with a number of adaptations. First, texts are indexed and prepared to be processed by frequent itemset levelwise search. Association rules are then extracted and interpreted, with respect
On Monotone Data Mining Languages
 In Proc. of International Workshop on Database Programming Languages (DBPL
, 2001
"... We present a simple Data Mining Logic (DML) that can express common data mining tasks, like "Find Boolean association rules" or "Find inclusion dependencies." At the center of the paper is the problem of characterizing DML queries that are amenable to the levelwise search stra ..."
Abstract
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We present a simple Data Mining Logic (DML) that can express common data mining tasks, like "Find Boolean association rules" or "Find inclusion dependencies." At the center of the paper is the problem of characterizing DML queries that are amenable to the levelwise search
4S: Scalable Subspace Search Scheme Overcoming Traditional Apriori Processing
"... AbstractIn many realworld applications, data is collected in multidimensional spaces. However, not all dimensions are relevant for data analysis. Instead, interesting knowledge is hidden in correlated subsets of dimensions (i.e., subspaces of the original space). Detecting these correlated subsp ..."
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scalable subspace search scheme (4S), which overcomes the efficiency problem by departing from the traditional levelwise search. We propose a new generalized notion of correlated subspaces which gives way to transforming the search space to a correlation graph of dimensions. Then we perform a direct mining
Results 1  10
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