Analysing inconsistent information using distance-based measures

Abstract

There have been a number of proposals for measuring inconsistency in a knowledgebase (i.e. a set of logical formulae). These include measures that consider the minimally inconsistent subsets of the knowledgebase, and measures that consider the paraconsistent models (3 or 4 valued models) of the knowledgebase. In this paper, we present a new approach that considers the amount by which each formula has to be weakened in order for the knowledgebase to be consistent. This approach is based on ideas of knowledge merging by Konienczny and Pino-Perez. We show that this approach gives us measures that are different from existing measures, that have desirable properties, and that can take the significance of inconsistencies into account. The latter is useful when we want to differentiate between inconsistencies that have minor significance from inconsistencies that have major significance. We also show how our measures are potentially useful in applications such as evaluating violations of integrity constraints in databases and for deciding how to act on inconsistency.

Measuring and Computing Database Inconsistency via Repairs

“…based on the Jaccard distance [14]. Several measures have been considered in knowledge representation [10,16,9], mostly for the propositional case. It would be interesting to analyze the general properties of those measures that are closer to database applications, along the lines of [8]; and their relationships.…”

An Empirical Perusal of Dis tance Measures for Clustering with Big Data Mining

“…(A4) In present time various distance measure is available for clustering and these distance measure groups under Minkowski, L(1), L(2), Inner product, Shannon's entropy, Combination, Intersection and Fidelity family [4][14] [35]. In this section, the paper describes various distance measures under these families [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] [32][33][34][35].…”

“…Distance measures are not only essential to solve the clustering problem, but it is also solved to pattern recognition, classification, retrieval related problems [4], help to the derivation of new distance measure [5], text classification and clustering [6], document content comparison [7], time-series data management [8], uncertain data classification [9] and clustering [l0], bio-cryptic authentication in cloud databases [11], spatial concentration [12], location fingerprinting [13], author profiling [14], combining density [15], heavy aggregation operators [16], analyzing inconsistent information [17], network intrusion anomaly detection [18] for high volume, variety and velocity. The objective of this paper is identifying the best cluster distance measure for cluster creation in the big data mining and this objective is obtained by the six sections.…”

Localising iceberg inconsistencies

“…To answer the second question in a qualitative way, inconsistent knowledge bases were classified by the severity of their inconsistency [17]. Recently, to numerically quantify the extent to which a knowledge base is inconsistent, many inconsistency measures have been proposed [29,24,25,19,28,27,20,42,43]. In contrast, the first question appears quite underdeveloped, and it is the subject of the present work.…”