Hierarchical Clustering of Composite Objects with a Variable Number of Components

Ketterlin, Alain; Gançarski, Pierre; Korczak, Jerzy

doi:10.1007/978-1-4612-2404-4_22

Cited by 3 publications

(6 citation statements)

References 5 publications

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“…We assume that an observation is a vector of nominal values, V ij along distinct variables, A i . A measure of category utility (Gluck & Corter, 1985;Corter & Gluck, 1992), CU(C k ) = P(C k ) X i X j P(A i = V ij jC k ) 2 P(A i = V ij ) 2 ]; and/or variants have been used extensively by a system known as Cobweb (Fisher, 1987a) and many related systems (Gennari, Langley, & Fisher, 1989;McKusick & Thompson, 1990;Iba & Gennari, 1991;McKusick & Langley, 1991;Reich & Fenves, 1991;Biswas, Weinberg, & Li, 1994;De Alte Da Veiga, 1994;Kilander, 1994;Ketterlin, Gan carski, & Korczak, 1995). This measure rewards clusters, C k , that increase the predictability of variable values within C k (i.e., P(A i = V ij jC k )) relative to their predictability in the population as a whole (i.e., P(A i = V ij )).…”

Section: An Objective Functionmentioning

confidence: 99%

Iterative Optimization and Simplification of Hierarchical Clusterings

Fisher¹

1996

jair

140

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Clustering is often used for discovering structure in data. Clustering systems di er in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a`tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modi es an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been constructed it is judged by analysts { often according to task-speci c criteria. Several authors have abstracted these criteria and posited a generic performance task akin to pattern completion, where the error rate over completed patterns is used to`externally' judge clustering utility. Given this performance task, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus promising to ease post-clustering analysis. Finally, we propose a number of objective functions, based on attribute-selection measures for decision-tree induction, that might perform well on the error rate and simplicity dimensions.

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Section: An Objective Functionmentioning

confidence: 99%

Iterative Optimization and Simplification of Hierarchical Clusterings

Fisher¹

1996

jair

140

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“…As collections may have variable cardinality, they cannot be compared directly. Some work on multi-valued features has been done in different fields within the frame of machine learning: in [12] the multiple associations between individuals has been studied for concept formation purposes, whereas the utility of set-valued primitive attributes in decision tree induction is discussed in [7].…”

Section: Classification-related Problemsmentioning

confidence: 99%

“…For example, let L1 = (5, 3, 6) and L~ = (2, 4, 6, 2, 7) be lists on the integer domain D -- [2,12]. For this couple of lists, the heuristic algorithm will provide a matching Ml = {(5, 2), (3,4), (6,6)} with total dissimilarity between matched elements of 4/10.…”

Section: List-valued Attributesmentioning

confidence: 99%

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Dissimilarity measure for collections of objects and values

Valtchev

Euzenat

1997

Advances in Intelligent Data Analysis Reasoning About Data

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Automatic classification may be used in object knowledge bases in order to suggest hypothesis about the structure of the available object sets. Yet its direct application meets some difficulties due to the way data is represented: attributes relating objects, multi-valued attributes, non-standard and external data types used in object descriptions. We present here an approach to the automatic classification of objects based on a specific dissimilarity model. The topological measure, presented in a previous paper, accounts for both object relations and the variety of available data types. In this paper, the extension of the topological measure on multi-valued object attributes, e.g. lists or sets, is presented. The resulting dissimilarity is completely integrated in the knowledge model TROPES which enables the definition of a classification strategy for an arbitrary knowledge base built on top of TROPES.

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“…1. Aiming at generating a concept hierarchy, most of conceptual clustering methods are based on the method hierarchical agglomerative clustering (HAC), such a COBWEB [19]. The order approach is based on FCA result, such as FOGA [20].…”

Section: Llcc: Logic-based Lattice Conceptual Clustering Algorithmmentioning

confidence: 99%

Viral Logical Concept Analysis for Malware Conceptual Hierarchy Generation

Binh¹

2017

IJMLC

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Abstract-Automatic classification of virus samples into a concept hierarchy has been attracting much attention from malware research community. This would help anti-virus experts to have an obvious and systematic view on the landscape of virus samples, whose numbers have been rapidly increasing recently. However, it is not a trivial work, since malwares usually come in binary forms whose actions are complicated and obfuscated. Therefore, the typical data mining approaches based on feature extraction are not easily applied. In this paper, we introduce an approach using Formal Concept Analysis (FCA) to generate a malware hierarchy. Since virus behaviours are often described effectively by temporal logic, we extend formal paradigm of FCA by using Logical Concept Analysis (LCA), where concepts are generalized by logic formulas. We also enhance the basic LCA to Viral Logical Concept Analysis (V-LCA), where abstraction techniques are used to abstract formal concepts representing virus samples. Our approach has been applied in a real dataset of virus and promising experiment results have been acquired. Index Terms-Computer virus, malicious software, malware detection, formal concept analysis, logical concept analysis, viral logical concept analysis, conceptual clustering.

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Hierarchical Clustering of Composite Objects with a Variable Number of Components

Cited by 3 publications

References 5 publications

Iterative Optimization and Simplification of Hierarchical Clusterings

Iterative Optimization and Simplification of Hierarchical Clusterings

Dissimilarity measure for collections of objects and values

Viral Logical Concept Analysis for Malware Conceptual Hierarchy Generation

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