Three Companions for Data Mining in First Order Logic

Raedt, Luc De; Blockeel, Hendrik; Dehaspe, Luc; Laer, Wim Van

doi:10.1007/978-3-662-04599-2_5

Cited by 23 publications

(20 citation statements)

References 14 publications

(9 reference statements)

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“…There are many more, for example from the rich literature of inductive logic programming (ILP) (e.g. Flach and Lachiche (1999); Raedt et al (2001); Dzeroski and Lavrac (2001); Kramer et al (2001); Domingos and Richardson (2004)), or based on using relational database joins to generate relational features (e.g. ; Popescul and Ungar (2003); Perlich and Provost (2004)).…”

Section: Prior Workmentioning

confidence: 99%

Classificaiton in Networked Data: A Toolkit and a Univariate Case Study

Macskassy¹,

Provost²

2005

200

486

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This paper presents NetKit, a modular toolkit for classification in networked data, and a case-study of its application to a collection of networked data sets used in prior machine learning research. Networked data are relational data where entities are interconnected, and this paper considers the common case where entities whose labels are to be estimated are linked to entities for which the label is known. NetKit is based on a three-component framework, comprising a local classifier, a relational classifier, and a collective inference procedure. Various existing relational learning algorithms can be instantiated with appropriate choices for these three components and new relational learning algorithms can be composed by new combinations of components. The case study demonstrates how the toolkit facilitates comparison of different learning methods (which so far has been lacking in machine learning research). It also shows how the modular framework allows analysis of subcomponents, to assess which, whether, and when particular components contribute to superior performance. The case study focuses on the simple but important special case of univariate network classification, for which the only information available is the structure of class linkage in the network (i.e., only links and some class labels are available). To our knowledge, no work previously has evaluated systematically the power of class-linkage alone for classification in machine learning benchmark data sets. The results demonstrate clearly that simple network-classification models perform remarkably well-well enough that they should be used regularly as baseline classifiers for studies of relational learning for networked data. The results also show that there are a small number of component combinations that excel, and that different components are preferable in different situations, for example when few versus many labels are known.

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Section: Prior Workmentioning

confidence: 99%

Classificaiton in Networked Data: A Toolkit and a Univariate Case Study

Macskassy¹,

Provost²

2005

200

486

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show abstract

“…Apart from this, there has been an active community focusing on machine learning techniques and predictive modeling for networked data, in particular research in statistical relational learning [7,9,10,[14][15][16][17]55]. This literature does not explicitly discuss predictive modeling based on social network data, but networks can be analyzed in a multi-relational data model so that the techniques are potential candidates for churn prediction based on customer networks.…”

Section: Related Literature On Churn and Wommentioning

confidence: 99%

“…It attempts to learn and reason from complex relational and probabilistic structures. There have been a number of advances in SRL including conditional random fields [60], relational dependency networks [51], Bayesian logic programs [29], relational association rules, regression trees in first-order logic, and relational decision trees [7].…”

Section: Statistical Relational Learning and Markov Logic Networkmentioning

confidence: 99%

Estimating the effect of word of mouth on churn and cross-buying in the mobile phone market with Markov logic networks

Dierkes

Bichler

Krishnan

2011

Decision Support Systems

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Much has been written about word of mouth and customer behavior. Telephone call detail records provide a novel way to understand the strength of the relationship between individuals. In this paper, we predict using call detail records the impact that the behavior of one customer has on another customer's decisions. We study this in the context of churn (a decision to leave a communication service provider) and cross-buying decisions based on an anonymized data set from a telecommunications provider.Call detail records are represented as a weighted graph and a novel statistical learning technique, Markov Logic Networks, is used in conjunction with logit models based on lagged neighborhood variables to develop the predictive model. In addition, we propose an approach to propositionalization tailored to predictive modeling with social network data. The results show that information on the churn of network neighbors has a significant positive impact on the predictive accuracy and in particular the sensitivity of churn models. The results provide evidence that word of mouth has a considerable impact on customers' churn decisions and also on the purchase decisions, leading to a 19.5% and 8.4% increase in sensitivity of predictive models.

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“…In recent years the data-cleaning challenge has attracted numerous efforts both in the industry as well as academia [Raedt et al 2001;Getoor 2001;Cohen and Richman 2002;Monge and Elkan 1996;Gravano et al 2001;Verykios et al 2003;Christen et al 2002;Cohen 1998;Sarawagi and Bhamidipaty 2002;Ristad and Yianilos 1998;Cohen et al 2003]. In this section, we present an overview of the existing work most related to the RELDC approach proposed in this article.…”

Section: Related Workmentioning

confidence: 99%

Domain-independent data cleaning via analysis of entity-relationship graph

Kalashnikov

Mehrotra

2006

ACM Trans. Database Syst.

145

116

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In this article, we address the problem of reference disambiguation. Specifically, we consider a situation where entities in the database are referred to using descriptions (e.g., a set of instantiated attributes). The objective of reference disambiguation is to identify the unique entity to which each description corresponds. The key difference between the approach we propose (called RELDC) and the traditional techniques is that RELDC analyzes not only object features but also inter-object relationships to improve the disambiguation quality. Our extensive experiments over two real data sets and over synthetic datasets show that analysis of relationships significantly improves quality of the result.

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Three Companions for Data Mining in First Order Logic

Cited by 23 publications

References 14 publications

Classificaiton in Networked Data: A Toolkit and a Univariate Case Study

Classificaiton in Networked Data: A Toolkit and a Univariate Case Study

Estimating the effect of word of mouth on churn and cross-buying in the mobile phone market with Markov logic networks

Domain-independent data cleaning via analysis of entity-relationship graph

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