Context-Aware User and Service Profiling by Means of Generalized Association Rules

Baralis, Elena; Cagliero, Luca; Cerquitelli, Tania; Garza, Paolo; Marchetti, M.

doi:10.1007/978-3-642-04592-9_7

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…Such two rules are alike in human interpretation of a schoolage child, whereas they would fight against each other as far as a frequency rating is concerned. Meanwhile, rules become more frequent should their semantic generalization be performed [1]. KRAMER helps finding abstractions of similar meta-situations.…”

Section: Generalization Algorithmmentioning

confidence: 99%

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Collaborative Context Experience in a Phonebook

Szczerbak

Toutain

Bouabdallah

et al. 2012

2012 26th International Conference on Advanced Information Networking and Applications Workshops

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Starting from a context-aware phonebook application, which allows users to see their contacts' situation, we introduce a collaborative context experience system. Users independently create notification rules, describing phonebookbased contextual situations for which they wish to receive notifications. The collaborative system gathers such rules from all users, and identifies important ones, that is, frequently declared ones. These rules are then processed to allow for generalization, and provided to users who can encounter a similar contextual situation. Similarities for this collaborative knowledge sharing mechanism are processed by applying semantic technology.

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Section: Generalization Algorithmmentioning

confidence: 99%

“…Such a system could be given a context taken from a group of users to perform its semantic generalization in order to derive frequent patterns [1]. It becomes possible to reason on abstract concepts should the semantic similarity measures be applied.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Context Experience in a Phonebook

Szczerbak

Toutain

Bouabdallah

et al. 2012

2012 26th International Conference on Advanced Information Networking and Applications Workshops

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“…An opportunistic aggregation approach [5] is exploited to evaluate the taxonomy, i.e., an itemset is generalized only if it is infrequent with respect to the minimum support threshold. Unlike previous works [5,6,8], the itemset mining algorithm used by CoGAR is also able to evaluate multiple hierarchies on the same attribute to analyze at the same time different facets (i.e., aggregation hierarchies) of the same feature. To effectively support end-users in exploring the extracted knowledge, mined patterns and multiple-taxonomies are stored in XML files that can be queried by means of XQuery [22].…”

Section: This Paper Presents the Cogar (Constrained Generalized Assocmentioning

confidence: 99%

“…The proposed approach is orthogonal with respect to the aforementioned ones [4,18] as it could be applied to select the most statistically significant patterns among those of analyst's interest. Finally, the idea of exploiting generalized association rules for contextaware user and service profiling and for network traffic analysis was firstly introduced in [6] and [3] respectively. The focus in [6] is on profiling users and services in a mobile context-aware application, while in [3] is on the characterization of stream network data.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the idea of exploiting generalized association rules for contextaware user and service profiling and for network traffic analysis was firstly introduced in [6] and [3] respectively. The focus in [6] is on profiling users and services in a mobile context-aware application, while in [3] is on the characterization of stream network data. Both in [6] and [3], the mining activity is performed by exploiting the GenIO algorithm [5], while the CoGAR framework exploits a different mining algorithm and enforce different constraints (schema constraints and the opportunistic confidence constraint) to remove uninteresting or redundant patterns.…”

Section: Related Workmentioning

confidence: 99%

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Generalized association rule mining with constraints

Baralis

Cagliero

Cerquitelli

et al. 2012

Information Sciences

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Generalized association rule extraction is a powerful tool to discover a high level view of the interesting patterns hidden in the analyzed data. However, since the patterns are extracted at any level of abstraction, the mined rule set may be too large to be effectively exploited in the decision making process. Thus, to discover valuable and interesting knowledge a post-processing step is usually required. This paper presents the COGAR framework to efficiently support constrained generalized association rule mining. The generalization process of COGAR exploits a (user-provided) multiple-taxonomy to drive an opportunistic itemset generalization process, which prevents discarding relevant but infrequent knowledge by aggregating features at different granularity levels. Besides the traditional support and confidence constraints, two further constraints are enforced: (i) schema constraints and (ii) the opportunistic confidence constraint. Schema constraints allow the analyst to specify the structure of the patterns of interest and drive the itemset mining phase. The opportunistic confidence constraint, a new constraint proposed in this paper, allows us to discriminate between significant and redundant rules by analyzing similar rules belonging to different abstraction levels. This constraint is enforced during the rule generation step. Experiments performed on real datasets collected in two different application domains show the effectiveness and the efficiency of the proposed framework in mining constrained generalized association rules

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Discovering Contextual Association Rules in Relational Databases

Quintarelli

Rabosio

2014

Advances in Intelligent Systems and Computing

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Contextual association rules represent co-occurrences between contexts and properties of data, where the context is a set of environmental or user personal features employed to customize an application. Due to their particular structure, these rules can be very tricky to mine, and if the process is not carried out with care, an unmanageable set of not significant rules may be extracted. In this paper we survey two existing algorithms for relational databases and present a novel algorithm that merges the two proposals overcoming their limitations

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Context-Aware User and Service Profiling by Means of Generalized Association Rules

Cited by 13 publications

References 12 publications

Collaborative Context Experience in a Phonebook

Collaborative Context Experience in a Phonebook

Generalized association rule mining with constraints

Discovering Contextual Association Rules in Relational Databases

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