Results 1  10
of
58
Computing Iceberg Concept Lattices with TITANIC
, 2002
"... We introduce the notion of iceberg concept lattices... ..."
Abstract

Cited by 83 (13 self)
 Add to MetaCart
We introduce the notion of iceberg concept lattices...
Discovering significant patterns
, 2007
"... Pattern discovery techniques, such as association rule discovery, explore large search spaces of potential patterns to find those that satisfy some userspecified constraints. Due to the large number of patterns considered, they suffer from an extreme risk of type1 error, that is, of finding patter ..."
Abstract

Cited by 41 (3 self)
 Add to MetaCart
Pattern discovery techniques, such as association rule discovery, explore large search spaces of potential patterns to find those that satisfy some userspecified constraints. Due to the large number of patterns considered, they suffer from an extreme risk of type1 error, that is, of finding patterns that appear due to chance alone to satisfy the constraints on the sample data. This paper proposes techniques to overcome this problem by applying wellestablished statistical practices. These allow the user to enforce a strict upper limit on the risk of experimentwise error. Empirical studies demonstrate that standard pattern discovery techniques can discover numerous spurious patterns when applied to random data and when applied to realworld data result in large numbers of patterns that are rejected when subjected to sound statistical evaluation. They also reveal that a number of pragmatic choices about how such tests are performed can greatly affect their power.
A survey on condensed representations for frequent sets
 In: Constraint Based Mining and Inductive Databases, SpringerVerlag, LNAI
, 2005
"... Abstract. Solving inductive queries which have to return complete collections of patterns satisfying a given predicate has been studied extensively the last few years. The specific problem of frequent set mining from potentially huge boolean matrices has given rise to tens of efficient solvers. Freq ..."
Abstract

Cited by 30 (4 self)
 Add to MetaCart
Abstract. Solving inductive queries which have to return complete collections of patterns satisfying a given predicate has been studied extensively the last few years. The specific problem of frequent set mining from potentially huge boolean matrices has given rise to tens of efficient solvers. Frequent sets are indeed useful for many data mining tasks, including the popular association rule mining task but also feature construction, associationbased classification, clustering, etc. The research in this area has been boosted by the fascinating concept of condensed representations w.r.t. frequency queries. Such representations can be used to support the discovery of every frequent set and its support without looking back at the data. Interestingly, the size of condensed representations can be several orders of magnitude smaller than the size of frequent set collections. Most of the proposals concern exact representations while it is also possible to consider approximated ones, i.e., to trade computational complexity with a bounded approximation on the computed support values. This paper surveys the core concepts used in the recent works on condensed representation for frequent sets. 1
Reasoning about Sets using Redescription Mining
 KDD'05
, 2005
"... Redescription mining is a newly introduced data mining problem that seeks to find subsets of data that afford multiple definitions. It can be viewed as a generalization of association rule mining, from finding implications to equivalences; as a form of conceptual clustering, where the goal is to ide ..."
Abstract

Cited by 19 (10 self)
 Add to MetaCart
Redescription mining is a newly introduced data mining problem that seeks to find subsets of data that afford multiple definitions. It can be viewed as a generalization of association rule mining, from finding implications to equivalences; as a form of conceptual clustering, where the goal is to identify clusters that afford dual characterizations; and as a form of constructive induction, to build features based on given descriptors that mutually reinforce each other. In this paper, we present the use of redescription mining as an important tool to reason about a collection of sets, especially their overlaps, similarities, and differences. We outline algorithms to mine all minimal (nonredundant) redescriptions underlying a dataset using notions of minimal generators of closed itemsets. We also show the use of these algorithms in an interactive context, supporting constraintbased exploration and querying. Specifically, we showcase a bioinformatics application that empowers the biologist to define a vocabulary of sets underlying a domain of genes and to reason about these sets, yielding significant biological insight.
Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis
, 2001
"... Association rules are used to investigate large databases. The analyst is usually confronted with large lists of such rules and has to find the most relevant ones for his purpose. Based on results about knowledge representation within the theoretical framework of Formal Concept Analysis, we present ..."
Abstract

Cited by 17 (7 self)
 Add to MetaCart
Association rules are used to investigate large databases. The analyst is usually confronted with large lists of such rules and has to find the most relevant ones for his purpose. Based on results about knowledge representation within the theoretical framework of Formal Concept Analysis, we present relatively small bases for association rules from which all rules can be deduced. We also provide algorithms for their calculation.
Inductive databases and multiple uses of frequent itemsets: the cInQ approach
 In Database Technologies for Data Mining  Discovering Knowledge with Inductive Queries, volume 2682 of LNCS
, 2004
"... Abstract. Inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. With an IDB the user/analyst performs a set of very different operations on data using a query language, powerful enough to perform all the required elaborations, such as data pr ..."
Abstract

Cited by 14 (8 self)
 Add to MetaCart
Abstract. Inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. With an IDB the user/analyst performs a set of very different operations on data using a query language, powerful enough to perform all the required elaborations, such as data preprocessing, pattern discovery and pattern postprocessing. We present a synthetic view on important concepts that have been studied within the cInQ European project when considering the pattern domain of itemsets. Mining itemsets has been proved useful not only for association rule mining but also feature construction, classification, clustering, etc. We introduce the concepts of pattern domain, evaluation functions, primitive constraints, inductive queries and solvers for itemsets. We focus on simple highlevel definitions that enable to forget about technical details that the interested reader will find, among others, in cInQ publications. 1
Closed Sets for Labeled Data ⋆
"... Abstract. Closed sets are being successfully applied in the context of compacted data representation for association rule learning. However, their use is mainly descriptive. This paper shows that, when considering labeled data, closed sets can be adapted for prediction and discrimination purposes by ..."
Abstract

Cited by 13 (0 self)
 Add to MetaCart
Abstract. Closed sets are being successfully applied in the context of compacted data representation for association rule learning. However, their use is mainly descriptive. This paper shows that, when considering labeled data, closed sets can be adapted for prediction and discrimination purposes by conveniently contrasting covering properties on positive and negative examples. We formally justify that these sets characterize the space of relevant combinations of features for discriminating the target class. In practice, identifying relevant/irrelevant combinations of features through closed sets is useful in many applications. Here we apply it to compacting emerging patterns and essential rules and to learn descriptions for subgroup discovery. 1
ZART: A Multifunctional Itemset Mining Algorithm
"... Abstract. In this paper 3, we present platform Coron, which is a domain independent, multipurposed data mining platform, incorporating a rich collection of data mining algorithms. One of these algorithms is a multifunctional itemset mining algorithm called Zart, which is based on the Pascal algorit ..."
Abstract

Cited by 12 (9 self)
 Add to MetaCart
Abstract. In this paper 3, we present platform Coron, which is a domain independent, multipurposed data mining platform, incorporating a rich collection of data mining algorithms. One of these algorithms is a multifunctional itemset mining algorithm called Zart, which is based on the Pascal algorithm, with some additional features. In particular, Zart is able to perform the following, usually independent, tasks: identify frequent closed itemsets and associate generators to their closures. This allows one to find minimal nonredundant association rules. At present, Coron appears to be an original working platform, integrating efficient algorithms for both itemset and association rule extraction, allowing a number of auxiliary operations for preparing and filtering data, and, for interpreting the extracted units of knowledge. 1
Relative risk and odds ratio: A data mining perspective
 In PODS
, 2005
"... We are often interested to test whether a given cause has a given effect. If we cannot specify the nature of the factors involved, such tests are called modelfree studies. There are two major strategies to demonstrate associations between risk factors (ie. patterns) and outcome phenotypes (ie. clas ..."
Abstract

Cited by 12 (5 self)
 Add to MetaCart
We are often interested to test whether a given cause has a given effect. If we cannot specify the nature of the factors involved, such tests are called modelfree studies. There are two major strategies to demonstrate associations between risk factors (ie. patterns) and outcome phenotypes (ie. class labels). The first is that of prospective study designs, and the analysis is based on the concept of “relative risk”: What fraction of the exposed (ie. has the pattern) or unexposed (ie. lacks the pattern) individuals have the phenotype (ie. the class label)? The second is that of retrospective designs, and the analysis is based on the concept of “odds ratio”: The odds that a case has been exposed to a risk factor is compared to the odds for a case that has not been exposed. The efficient extraction of patterns that have good relative risk and/or odds ratio has not been previously studied in the data mining context. In this paper, we investigate such patterns. We show that this pattern space can be systematically stratified into plateaus of convex spaces based on their support levels. Exploiting convexity, we formulate a number of sound and complete algorithms to extract the most general and the most specific of such patterns at each support level. We compare these algorithms. We further demonstrate that the most efficient among these algorithms is able to mine these sophisticated patterns at a speed comparable to that of mining frequent closed patterns, which are patterns that satisfy considerably simpler conditions. 1.
Query languages supporting descriptive rule mining: A comparative study
 In Database Technologies for Data Mining  Discovering Knowledge with Inductive Queries, volume 2682 of LNCS
, 2004
"... Abstract. Recently, inductive databases (IDBs) have been proposed to tackle the problem of knowledge discovery from huge databases. With an IDB, the user/analyst performs a set of very different operations on data using a query language, powerful enough to support all the required manipulations, suc ..."
Abstract

Cited by 11 (4 self)
 Add to MetaCart
Abstract. Recently, inductive databases (IDBs) have been proposed to tackle the problem of knowledge discovery from huge databases. With an IDB, the user/analyst performs a set of very different operations on data using a query language, powerful enough to support all the required manipulations, such as data preprocessing, pattern discovery and pattern postprocessing. We provide a comparison between three query languages (MSQL, DMQL and MINE RULE) that have been proposed for descriptive rule mining and discuss their common features and differences. These query languages look like extensions of SQL. We present them using a set of examples, taken from the real practice of rule mining. In the paper we discuss also OLE DB for Data Mining and Predictive Model Markup Language, two recent proposals that like the first three query languages respectively provide native support to data mining primitives and provide a description in a standard language of statistical and data mining models. 1