Similarity based applications for data-driven concept and word models based on type-1 and type-2 fuzzy sets

Abstract

Abstract-In this paper we explore the practical application of the previously introduced approach [1] to generate fuzzy sets from interval-valued data. We demonstrate two specific example applications where we 1) generate type-1 fuzzy sets from intervalvalued survey data for both words (e.g., neutral, excellent) and concepts (e.g., ambience, food) and 2) generate zSlices based general type-2 fuzzy set valued data from multiple iterations of a survey. We highlight the need for the simultaneous rating of both concepts and words in order to maintain context (including timeliness) of the resulting models. Further, in both example applications, we demonstrate using the Jaccard similarity measure how similarity measures can be employed to both relate and attribute word models to concept models (e.g., excellent food) and compare different concepts directly for different contexts (e.g., ambience in venue A vs. ambience in venue B). We provide interpretations for the resulting word/concept models and similarity values and highlight their utility, for example, for the data-driven generation of linguistic descriptions of venues. Finally, we highlight remaining questions and challenges both in technical terms and in application terms.

Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance

“…This is due to a large difference between the range of elements contained within the sets, which increases the dissimilarity according to the measure. In both cases, the FS B covers the range [1,5] whereas A only covers [1,2] in c, and [4,5] in d. It can also now be observed, by using the comparative measure alone, that B is to the right of A in c, and is to the left of A in d. The ordering and direction of the distance between c and d are the same using both the DM and the comparative measure.…”

“…For example, similarity has often been used to measure the similarity between different word models [1], [2], or to find similar patterns in classification [3] and clustering [3]. Distance Measures (DMs), though less commonly researched, have been used to compare FSs, for example, in the ranking of fuzzy numbers [4].…”

“…Current research within the literature has generally made a choice between using either measures of similarity or distance, however in many cases, it is not trivial to make this choice, in particular when FSs are dynamically created from data such as for approaches like [2] and [5]. This paper proposes the fusion of both measures into a single measure which can be applied in the comparison of FSs and produces meaningful results regardless of the exact nature of the FSs to be measured.…”

Exploring differences in interpretation of words essential in medical expert-patient communication

“…It is built on top of the work presented in [12], where an agreementbased method [19] of capturing interval-valued survey data is demonstrated. Also, in [13] its practical application along with the use of a similarity measures to relate attribute word models to concept models is explored.…”

“…On the other hand, we explore the practical application of the IAA [6], [12], [13] to capture interval-valued data and generate FS models associated with such words.…”