Efficiency analsyis of concept lattice construction algorithms

Kovács, László

doi:10.1016/j.promfg.2018.03.003

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…, where | | denotes the number of concepts and | | represents the number of entries in the formal context. This approximation accommodates not merely the number of objects or attributes but also the overall size of the context, providing a potentially more precise estimate of the concept count [26].…”

Section: A K-means Dijkstra On Lattice (Kdl)mentioning

confidence: 99%

K-Means Extensions for Clustering Categorical Data on Concept Lattice

Alwersh,

Kovács

2023

IJACSA

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Formal Concept Analysis (FCA) is a key tool in knowledge discovery, representing data relationships through concept lattices. However, the complexity of these lattices often hinders interpretation, prompting the need for innovative solutions. In this context, the study proposes clustering formal concepts within a concept lattice, ultimately aiming to minimize lattice size. To address this, The study introduces introduce two novel extensions of the k-means algorithm to handle categorical data efficiently, a crucial aspect of the FCA framework. These extensions, namely K-means Dijkstra on Lattice (KDL) and Kmeans Vector on Lattice (KVL), are designed to minimize the concept lattice size. However, the current study focuses on introducing and refining these new methods, laying the groundwork for our future goal of lattice size reduction. The KDL utilizes FCA to build a graph of formal concepts and their relationships, applying a modified Dijkstra algorithm for distance measurement, thus replacing the Euclidean distance in traditional k-means. The defined centroids are formal concepts with minimal intra-cluster distances, enabling effective categorical data clustering. In contrast, the KVL extension transforms formal concepts into numerical vectors to leverage the scalability offered by traditional k-means, potentially at the cost of clustering quality due to oversight of the data's inherent hierarchy. After rigorous testing, KDL and KVL proved robust in managing categorical data. The introduction and demonstration of these novel techniques lay the groundwork for future research, marking a significant stride toward addressing current challenges in categorical data clustering within the FCA framework.

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Section: A K-means Dijkstra On Lattice (Kdl)mentioning

confidence: 99%

K-Means Extensions for Clustering Categorical Data on Concept Lattice

Alwersh,

Kovács

2023

IJACSA

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“…The Batch algorithm is a kind of early construction algorithm. Based on the different ways of constructing the lattice, they can be divided into three categories, including top-down, bottom-up, and enumeration [14]. In the top-down method, the top node is constructed first, and then it proceeds downwards layer by layer.…”

Section: The Batch Algorithmmentioning

confidence: 99%

A Web Page Clustering Method Based on Formal Concept Analysis

2018

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Web page clustering is an important technology for sorting network resources. By extraction and clustering based on the similarity of the Web page, a large amount of information on a Web page can be organized effectively. In this paper, after describing the extraction of Web feature words, calculation methods for the weighting of feature words are studied deeply. Taking Web pages as objects and Web feature words as attributes, a formal context is constructed for using formal concept analysis. An algorithm for constructing a concept lattice based on cross data links was proposed and was successfully applied. This method can be used to cluster the Web pages using the concept lattice hierarchy. Experimental results indicate that the proposed algorithm is better than previous competitors with regard to time consumption and the clustering effect.

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Bit-Close: a fast incremental concept calculation method

Ke,

Li,

2024

Appl Intell

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Efficiency analsyis of concept lattice construction algorithms

Cited by 5 publications

References 12 publications

K-Means Extensions for Clustering Categorical Data on Concept Lattice

K-Means Extensions for Clustering Categorical Data on Concept Lattice

A Web Page Clustering Method Based on Formal Concept Analysis

Bit-Close: a fast incremental concept calculation method

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