On the mining of numerical data with Formal Concept Analysis and similarity
نویسندگان
چکیده
In this paper, we present a method based on Formal Concept Analysis (FCA) for mining numerical data. An adaptation of standard FCA Galois connection takes into account “similarity” between numerical values of attributes and leads to the definition of many-valued (MV) formal concepts and concept lattices. Depending on a similarity threshold, many-valued concept lattices have different levels of precision. Accordingly, multi-level classification tasks can be achieved such as classification, navigation, information retrieval, and data mining.
منابع مشابه
Estimations of Similarity in Formal Concept Analysis of Data with Graded Attributes
We study similarity in formal concept analysis of data tables with graded attributes. We focus on similarity related to formal concepts and concept lattices, i.e. the outputs of formal concept analysis. We present several formulas for estimation of similarity of outputs in terms of similarity of inputs. The results answer some problems which arose in previous investigation as well as some natur...
متن کاملRevisiting Numerical Pattern Mining with Formal Concept Analysis
We investigate the problem of mining numerical data with Formal Concept Analysis. The usual way is to use a scaling procedure –transforming numerical attributes into binary ones– leading either to a loss of information or of efficiency, in particular w.r.t. the volume of extracted patterns. By contrast, we propose to directly work on numerical data in a more precise and efficient way. For that,...
متن کاملخوشهبندی اسناد مبتنی بر آنتولوژی و رویکرد فازی
Data mining, also known as knowledge discovery in database, is the process to discover unknown knowledge from a large amount of data. Text mining is to apply data mining techniques to extract knowledge from unstructured text. Text clustering is one of important techniques of text mining, which is the unsupervised classification of similar documents into different groups. The most important step...
متن کاملA Geometric View of Similarity Measures in Data Mining
The main objective of data mining is to acquire information from a set of data for prospect applications using a measure. The concerning issue is that one often has to deal with large scale data. Several dimensionality reduction techniques like various feature extraction methods have been developed to resolve the issue. However, the geometric view of the applied measure, as an additional consid...
متن کاملExtracting Decision Trees from Interval Pattern Concept Lattices
Formal Concept Analysis (FCA) and concept lattices have shown their effectiveness for binary clustering and concept learning. Moreover, several links between FCA and unsupervised data mining tasks such as itemset mining and association rules extraction have been emphasized. Several works also studied FCA in a supervised framework, showing that popular machine learning tools such as decision tre...
متن کامل