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Nowadays Information Retrieval (IR) is difficult because of huge amount of information published on the Internet. So it is very relevant to organize documents based on its content. The proposed work address this issue by generating concepts from the documents and these documents are grouped based on a data mining approach. To generate the concept, keywords are extracted from the documents but the...
Expertise retrieval has already gained significant interest in the area of information retrieval due to multitude of concrete application contexts where search for specific experts is required. In this paper, we introduce a formal concept analysis approach for clustering of a group of experts with respect to given subject areas. Initially, the domain of interest is presented at some level of abstraction...
Stored data in database can hide some knowledge, which is interesting, useful to hidden knowledge discover. In this context, an algorithms number a frequent itemsets and association rules extraction were presented. Special feature of these algorithms is to generation a large number of rules, making their exploitation a difficult task. In this paper we will introduce a new algorithm for association...
Classification rule mining is one of important areas of data mining, and it is a hot subject at present. In this paper, we introduce some definitions: relevant concept, pseudo concept, and relevant concept cover. Then we presents an algorithm to compute relevant concept cover, which divides the context into subsets, calculate the set of relevant concepts by the value of concept shannon entropy, and...
This paper presents a new method for automatically extracting smartphone users' contextual behaviors from the digital traces collected during their interactions with their devices. Our goal is in particular to understand the impact of users' context (e.g., location, time, environment, etc.) on the applications they run on their smartphones. We propose a methodology to analyze digital traces and to...
In this paper we provide modification of approaches for extraction of keyphrases from single textual document (without external information) based on the hierarchical concepts created upon the text of particular document. For the creation of hierarchical concepts method from area of Formal Concept Analysis (FCA) is used, which organizes objects into concept lattice (structure of hierarchically organized...
In a world full of connections between people and objects, new needs arise requiring multidisciplinary analysis of these new networks. This work presents a approach to analyze an Internet Service Provider (ISP) database using a minimal cover of implications extracted from formal concept analysis and complex network techniques. Our goal is to analyze access to the 25 most visited websites to find access...
The problem of association rule mining is one of the most frequently studied and popular KDD tasks. Association rule mining is an important sub-branch of data mining. Based on the known current existence of association rule mining algorithms, this paper emphasizes the research work of deleting redundant association rules. It is a problem to mine quantitative association rules because the existing...
In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and...
With an increased interest in machine processable data, many datasets are now published in RDF (Resource Description Framework) format in Linked Data Cloud. These data are distributed over independent resources which need to be centralized and explored for domain specific applications. This paper proposes a new approach based on interactive data exploration paradigm using Pattern Structures, an extension...
Recently α-cut irreducible and δ1δ2-multi-adjoint concept lattices have been introduced as two different methodologies focus on reducing the size of a given fuzzy concept lattice. The philosophy of both methodologies is completely different and so, the obtained lattices too. This paper analyzes the differences and proposes that the best is to combine both methodologies in order to obtain new procedures...
The intent stability index represents an useful relevancy measure of the extracted patterns in the FCA data mining method. We describe this kind of relevancy measure for the so-called one-sided concept lattices, which represent a fuzzy generalization of the classical FCA. The basic properties of the stability index and some experimental results concerning this index conducted on randomly generated...
The goal of feature extraction in multimedia mining is to discover important features for represented into a form that can represent information of multimedia data. Sequential pattern is one form of data representation formed of a number of elements that appear in sequence. The goal of this study is to analyze sequential pattern representation performancy to improve accuracy and efficiency. Analysis...
Formal concept analysis and rough set theory have both similarities and differences. Although both are on the base of some data table, they provide two different methods for data mining and knowledge acquisition. At first, this paper discusses differences and relations between the extension of formal concepts in formal concept analysis and the equivalence classes of the rough set theory. Then, by...
We describe our web-based system for the analysis of students' results on the course Fundamentals of Electrical Engineering by applying the method of Formal Concept Analysis. We have focused on the students' answers and constructed their concept lattices or taxonomies of the subject matter. Finally, we have shown that this approach corresponds well with the actual students' overall results and final...
Fuzzy concept lattice generated from fuzzy context (L-context) has been used in a number of fields, such as data mining, decision-making, information retrieval and so on. Recent works have achieved great success. In this paper, given a binary relation, we generated the fuzzy concept lattice. In order to make decision-making much easier, reducing the number of attributes without a large amount of information...
Recently, there has been rapid growth in the volume of digital data from semi-structured and unstructured sources, such as web pages, images, videos, tweets, blog posts, and emails. The volume of data increases daily, making it difficult to access the data efficiently. To tackle the problems, we can represent the data in a summarised form using fuzzy formal concept lattices. In a situation where data...
Formal Concept Analysis is a theoretical framework which structures a set of objects described by properties. Formal Concept Analysis is a classification technique that takes data sets of objects and their attributes, and extracts relations between these objects according to the attributes they share. This structure reveals and categorizes commonalities and variability in a canonical form. From this...
Feature location is an activity to identify correspondence between features in a system and program elements in source code. After a feature is located, developers need to understand implementation structure around the location from static and/or behavioral points of view. This paper proposes a semi-automatic technique both for locating features and exposing their implementation structures in source...
Association rule discovery, as the kernel task of data mining, has been studied widely. However, most algorithms based on frequent item sets have to scan databases many times. This reduces the algorithms' efficiency. Formal concept analysis is a useful tool in many fields. In this paper, an association rule mining algorithm is proposed based on the formal concept analysis. Through analysis the relationship...
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