Experimental study on time complexity of GOSCL algorithm for sparse data tables

2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) Pub Date : 2012-05-24 DOI:10.1109/SACI.2012.6249984

P. Butka, J. Pócsová, J. Pócs

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引用次数: 7

Abstract

In this paper we provide experimental study on time complexity of GOSCL algorithm according to the sparseness of the input data table. GOSCL is incremental algorithm for the creation of Generalized One-Sided Concept Lattices, which is related to well-known Formal Concept Analysis area, but with the possibility to work with different types of attributes and to produce one-sided concept lattice from the generalized one-sided formal context. Generally, FCA-based algorithms are exponential. However, in practice there are many inputs for which the complexity is reduced. One of the special cases is related to the high number of "zeros" (bottom elements) in data table for so-called sparse data matrices, which is characteristic for some inputs like document-term matrix in text-mining analysis. We describe experimentally the influence of sparseness of data tables on time complexity of GOSCL with different distributions of zeros generated artificially randomly or according to the standard text-mining datasets.

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稀疏数据表GOSCL算法时间复杂度的实验研究

本文根据输入数据表的稀疏性对GOSCL算法的时间复杂度进行了实验研究。GOSCL是一种用于创建广义单侧概念格的增量算法，它与众所周知的形式概念分析领域有关，但可以处理不同类型的属性，并从广义单侧形式上下文中生成单侧概念格。一般来说，基于fca的算法是指数型的。然而，在实践中，有许多输入可以降低复杂性。其中一个特殊情况与所谓的稀疏数据矩阵的数据表中的大量“零”(底部元素)有关，这是文本挖掘分析中的文档术语矩阵等某些输入的特征。我们通过实验描述了数据表的稀疏性对GOSCL的时间复杂度的影响，这些GOSCL具有人工随机生成的零分布和根据标准文本挖掘数据集生成的零分布。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

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