Cherukuri Aswani Kumar

On rule acquisition in decision formal contexts

Rule acquisition is one of the main purposes in the analysis of decision formal contexts. Up to now, there have existed several types of rules (e.g., the decision rules and the granular rules) in decision formal contexts. This study firstly proposes a new algorithm with less time complexity for deriving the non-redundant decision rules from a decision formal context. Then, we invesigate decision rules and the granular rules in the consistent decision formal contexts and make a contrast between the decision rule oriented knowledge reduction and the granular rule oriented knowledge reduction. Finally, some experiments are conducted to assess the efficiency of the proposed rule acquisition algorithm.

Knowledge discovery in data using formal concept analysis and random projections

Knowledge discovery in data using formal concept analysis and random projections In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.

A comprehensive survey on formal concept analysis, its research trends and applications

Abstract In recent years, FCA has received significant attention from research communities of various fields. Further, the theory of FCA is being extended into different frontiers and augmented with other knowledge representation frameworks. In this backdrop, this paper aims to provide an understanding of the necessary mathematical background for each extension of FCA like FCA with granular computing, a fuzzy setting, interval-valued, possibility theory, triadic, factor concepts and handling incomplete data. Subsequently, the paper illustrates emerging trends for each extension with applications. To this end, we summarize more than 350 recent (published after 2011) research papers indexed in Google Scholar, IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and a few authoritative fundamental papers.

Comparison of reduction in formal decision contexts

Abstract In formal concept analysis, many reduction methods have recently been proposed for formal decision contexts, and each of them was to reduce formal decision contexts with a particular purpose. However, little attention has been paid to the comparison of their differences from various aspects. In fact, this problem is very important because it can provide evidence to select an appropriate reduction method for a given specific case. To address this problem, our study mainly focuses on clarifying the relationship among the existing reduction methods in formal decision contexts. Firstly, we give a rule-based review of the existing reduction methods, revealing the type of rules that each of them can preserve. Secondly, we analyze the relationship among the consistencies introduced by the existing reduction methods. More specifically, Weis first consistency (see [39] ) is stronger than others, while her second one is weaker than the remainder except Wus consistency (see [43] ). Finally, we make a comparison of the existing reductions, concluding that Lis reduction (see [14] ) maintaining the non-redundant decision rules of a formal decision context is coarser than others. The results obtained in this paper are beneficial for users to select an appropriate reduction method for meeting their requirements.

Fuzzy Formal Concept Analysis Approach for Information Retrieval

Recently Formal Concept Analysis (FCA), a mathematical framework based on partial ordering relations has become popular for knowledge representation and reasoning. Further this framework is extended as Fuzzy FCA, Rough FCA, etc. to deal with practical applications. There are investigations in the literature applying FCA for Information Retrieval (IR) applications. The objective of this paper is to apply Fuzzy FCA approach for IR. While adopting Fuzzy FCA, we follow a fast algorithm to generate the fuzzy concepts rather than classical algorithms that are based on residuated methods. Further we follow an approach that retrieves the relevant documents even during absence of exact match of the keywords.

Artificial Intelligence learning based on proportional navigation guidance

Artificial Intelligence (AI) is a vast domain with variety of applications. This research proposes the use of AI for guidance of an Unmanned Aerial Vehicle (UAV) to a maneuvering target. The AI agent is trained using machine learning algorithm. The PN guidance algorithm is used as a basis for training the agent. An orthogonal array based training strategy is proposed that provides a better training and reduced miss distance values. The AI based guidance provides performance equal to the PN Guidance law and in some cases it outperforms PN guidance law.

Modeling Associative Memories Using Formal Concept Analysis

Associative memory is one of the primary functions of the human brain. In the literature, there are several neural networks based models that represent associative memory with the help of pattern associations. In this paper, we model the associative memory activity using Formal Concept Analysis (FCA), which is a standard technique for data and knowledge processing. In our proposal, patterns are associated with the help of object-attribute relations and the memory is represented using the formal concepts generated using FCA. We show that the extent and intent relations in the concepts help us to recall the patterns bi-directionally. Further, we model the pattern recall process for the given input even when the exact match is not found in the memory, using the concept hierarchies in the concept lattice.