Symbolic Data Analysis and Formal Concept Analysis
نویسندگان
چکیده
Formal concept analysis (FCA) can be used for designing concept lattices from binary data for knowledge discovery purposes. Pattern structures in FCA are able to deal with complex data. In addition, this formalism provides a concise and an efficient algorithmic view of the formalism of symbolic data analysis (SDA).
منابع مشابه
Symbolic Objects in Formal Concept Analysis
Symbolic objects are the basic elements for knowledge representation in symbolic data analysis. This paper aims to integrate symbolic objects into formal concept analysis in order to compare and tie together both approaches.
متن کاملSymbolic Galois Lattices with Pattern Structures
Concept lattices are mathematical structures useful for many tasks in knowledge discovery and management. A concept lattice is basically obtained from binary data encoding the membership of some attributes to some objects. Dealing with complex data brings the important problem of discretization and the associated loss of information. To avoid discretization, (i) pattern structures and (ii) symb...
متن کاملA FCA-based Analysis of Sequential Care Trajectories
This paper presents a research work in the domains of sequential pattern mining and formal concept analysis. Using a combined method, we show how concept lattices and interestingness measures such as stability can improve the task of discovering knowledge in symbolic sequential data. We give example of a real medical application to illustrate how this approach can be useful to discover patterns...
متن کاملApplications of Fuzzy Program Graph in Symbolic Checking of Fuzzy Flip-Flops
All practical digital circuits are usually a mixture of combinational and sequential logic. Flip–flops are essential to sequential logic therefore fuzzy flip–flops are considered to be among the most essential topics of fuzzy digital circuit. The concept of fuzzy digital circuit is among the most interesting applications of fuzzy sets and logic due to the fact that if there has to be an ultimat...
متن کاملMachine Learning methods and applications using Formal Concept Analysis
Machine learning (ML) deals with algorithms that automatically improve with experience where the experience for a ML algorithm is huge repositories of data. Machine learning methods produce a program that fits data to a model from lots of examples that specify the correct output for a given input. Formal Concept Analysis (FCA) is a successful model of learning from positive and negative example...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017