Concept Relation Discovery and Innovation Enabling Technology (Cordiet)

Poelmans, Jonas; Elzinga, Paul; Viaene, Stijn; Dedene, Guido

doi:10.2139/ssrn.1713124

Cited by 5 publications

(3 citation statements)

References 19 publications

(12 reference statements)

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“…Concept discovery is a subfield of Knowledge Discovery in Databases (KDD) research that focuses on discovering insight into the underlying conceptual structure of the data [13]. The intuition is that such a structure can aid human users in understanding the data and solving various computation tasks and is especially important in critical domains [14,15,16].…”

Section: Concept Discovery From Unstructured Datamentioning

confidence: 99%

Subgroup Discovery in Unstructured Data

Arab¹,

Arora²,

Lu³

et al. 2022

Preprint

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Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest. Subgroup discovery has numerous applications in knowledge discovery and hypothesis generation, yet it remains inapplicable for unstructured, high-dimensional data such as images. This is because subgroup discovery algorithms rely on defining descriptive rules based on (attribute, value) pairs, however, in unstructured data, an attribute is not well defined. Even in cases where the notion of attribute intuitively exists in the data, such as a pixel in an image, due to the high dimensionality of the data, these attributes are not informative enough to be used in a rule. In this paper, we introduce the subgroup-aware variational autoencoder, a novel variational autoencoder that learns a representation of unstructured data which leads to subgroups with higher quality. Our experimental results demonstrate the effectiveness of the method at learning subgroups with high quality while supporting the interpretability of the concepts.Keywords Subgroup Discovery • Unstructured Data • Pattern Mining Despite numerous works in the literature, all subgroup discovery techniques suffer from a limitation -they are only applicable to structured data, i.e., data with well-defined attributes. A typical instance of this would be data found relational databases. However, in many settings, the data to be dealt with is unstructured and potentially high dimensional such as images, videos, text documents, and time series. Subgroups are defined through a conjunction of attribute and value pairs. For high dimensional data, such as an image, this implies that the subgroups will be defined based on the

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Section: Concept Discovery From Unstructured Datamentioning

confidence: 99%

Subgroup Discovery in Unstructured Data

Arab¹,

Arora²,

Lu³

et al. 2022

Preprint

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“…FCART follows a methodology from [38] to formalise iterative ontologydriven data analysis process and to implement several basic principles:…”

Section: Fca Tools and Practicementioning

confidence: 99%

“…In step 7, we used a leader in time efficiency, the algorithm FPmax* [216], from the competition organised in series of workshops on Frequent Itemset Mining Implementations (FIMI) 38 .…”

Section: Fca-based Approach For Near-duplicate Documents Detectionmentioning

confidence: 99%

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Ignatov

2015

Communications in Computer and Information Science

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Abstract. This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuS-SIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.

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FCA Analyst Session and Data Access Tools in FCART

Neznanov

Parinov

2014

Artificial Intelligence: Methodology, Systems, and Applications

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Concept Relation Discovery and Innovation Enabling Technology (Cordiet)

Cited by 5 publications

References 19 publications

Subgroup Discovery in Unstructured Data

Subgroup Discovery in Unstructured Data

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

FCA Analyst Session and Data Access Tools in FCART

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