Cellular neural networks for color image segmentation

In this paper we apply cellular neural networks for color image segmentation. Color aerial photographs will be analyzed. Two types of color models: RGB and HSV will be taken into account and compared. In resulting images we will distinguish some objects like houses, roads, trees and others. The selection of the objects will be based on the color value. We show that the choice of color model influences the results

A new insight into the linguistic summarization of time series via a degree of support:elimination of infrequent patterns

We extend our previous works on using a fuzzy logic based calculus of linguistically quantified propositions for linguistic summarization of time series (cf. Kacprzyk, Wilbik and Zadrożny [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. That approach, using the classic degree of truth (validity) to be maximized, is here extended by adding a degree of support. On the one hand, this can reflect in natural language the essence of traditional statistical approaches, and on the other hand, can help discard linguistic summaries with a high degree of truth but a low degree of support so that they concern infrequently occurring patterns and may be uninteresting. We show an application to the absolute performance analysis of an investment (mutual) fund

Linguistic summaries of time series : on some extended aggregation techniques

We further extend our approach to the linguistic summarization of time series (cf. Kacprzyk, Wilbik and Zadrony) in which an approach based on a calculus of linguistically quantified propositions is employed, and the essence of the problem is equated with a linguistic quantifier driven aggregation of partial scores (trends). We proceed towards a multicriteria analysis of summaries by assuming as a quality criterion Yager’s measure of informativeness that combines in a natural way the measures of truth, focus and specificity, to obtain a more advanced evaluation of summaries. The use of the informativeness measure for the purpose of a multicriteria evaluation of linguistic summaries of time series seems to be an effective and efficient approach, yet simple enough for practical applications. Results on the summarization of quotations of an investment (mutual) fund are very encouraging

Temporal linguistic summaries of time series using fuzzy logic

We consider linguistic summaries of time series used for an analysis of the past performance of investment (mutual) funds to help make future investment decisions. We use results from psychology, cognitive sciences and human decision making, which indicate a crucial role of time in the sense that means and ends, like decisions and outcomes, have a varying relevance and impact depending on the time when they occur, notably that what occurs in a more immediate past is more relevant and meaningful that what has occurred earlier. We propose to take into account some of psychological findings related to the importance of time by using different protoforms of linguistic summaries, temporal linguistic summaries, a substantial extension of the protoforms employed in our previous works. We consider two types of temporal protoforms exemplified by “Recently, among all segments, most are slowly increasing”, and exemplified by “Initially, among all short segments, most are quickly decreasing”. We compare them with the traditional ones, and present examples of their use for the analyses of investment funds

Comparison of time series via classic and temporal protoforms of linguistic summaries:an application to mutual funds and their benchmarks

We present a new approach to the evaluation of similarity of time series that are characterized by linguistic summaries. We consider so-called temporal data summaries, i.e. novel linguistic summaries that explicitly include a temporal aspect. We consider the case of a mutual (investment) fund and its underlying benchmark(s), and the new comparison method is based not on the comparison of the consecutive values or segments of the fund and its benchmark but on the comparison of classic and temporal linguistic summaries (i.e. based on a classic and temporal protoform) best describing their past behavior

Linguistic summarization of time series using fuzzy logic with linguistic quantifiers:a truth and specificity based approach

We reformulate and extend our previous works (cf. Kacprzyk, Wilbik and Zadrożny [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), mainly towards a more complex and realistic evaluation of results on the linguistic summarization of time series which is meant as the derivation of an linguistic quantifier driven aggregation of partial trends with respect to the dynamics of change, duration and variability. We use Zadeh’s calculus of linguistically quantified propositions but, in addition to the basic criterion of a degree of truth (validity), we also use a degree of specificity to make it possible to account for a frequent case that though the degree of truth of a very general (not specific) summary is high, its usefulness may be low due to its low specificity. We show an application to the absolute performance type analysis of daily quotations of an investment fund

A multi-criteria evaluation of linguistic summaries of time series via a measure of informativeness

We extend our works of deriving linguistic summaries of time series using a fuzzy logic approach to linguistic summarization. We proceed towards a multicriteria analysis of summaries by assuming as a quality criterion Yager’s measure of informativeness that combines in a natural way the measures of truth, focus and specificity, to obtain a more advanced evaluation of summaries. The use of the informativeness measure for the purpose of a multicriteria evaluation of linguistic summaries of time series seems to be an effective and efficient approach, yet simple enough for practical applications. Results on the summarization of quotations of an investment (mutual) fund are very encouraging

An extended, specificity based approach to linguistic summarization of time series

We reformulate and extend our previous works (cf. Kacprzyk, Wilbik and Zadrozny [7] – [17]), mainly towards a more complex evaluation of results on the linguistic summarization of time series meant as the derivation of an linguistic quantifier driven aggregation of partial trends with respect to the dynamics of change, duration and variability. We use Zadeh’s calculus of linguistically quantified propositions but, in addition to the basic criterion of a degree of truth (validity), we also use a degree of specificity to make it possible to account for a frequent case that though the degree of truth of a very general (not specific) summary is high, its usefulness may be low due to its low specificity. We show an application to the absolute performance type analysis of daily quotations of an investment fund

Linguistic summarization of event logs - a practical approach

The amount of data that is generated during the execution of a business process is growing. As a consequence it is increasingly hard to extract useful information from the large amount of data that is produced. Linguistic summarization helps to point business analysts in the direction of useful information, by verbalizing interesting patterns that exist in the data. In previous work we showed how linguistic summarization can be used to automatically generate diagnostic statements about event logs, such as ‘for most cases that contained the sequence ABC, the throughput time was long’. However, we also showed that our technique produced too many of these statements to be useful in a practical setting. Therefore this paper presents a novel technique for linguistic summarization of event logs, which generates linguistic summaries that are concise enough to be used in a practical setting, while at the same time enriching the summaries that are produced by also enabling conjunctive statements. The improved technique is based on pruning and clustering of linguistic summaries. We show that it can be used to reduce the number of summary statements 80–100% compared to previous work. In a survey among 51 practitioners, we found that practitioners consider linguistic summarization useful and easy to use and intend to use it if it were commercially available