Collaborative Knowledge Discovery &amp; Data Mining: From Knowledge to Experience

Horeis, T.; Sick, Bernhard

doi:10.1109/cidm.2007.368905

Cited by 16 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our future work includes applications of SwiftRule, e.g., in the fields of organic computing [57], collaborative knowledge discovery [58], or handwriting analysis [59]. We want to extend the techniques, e.g., by replacing the generative classifier by hidden Markov models with Gaussian output distributions.…”

Section: Discussionmentioning

confidence: 99%

SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

Fisch

Gruber

Sick

2011

IEEE Trans. Knowl. Data Eng.

Self Cite

View full text Add to dashboard Cite

In this article, we provide a new technique for temporal data mining which is based on classification rules that can easily be understood by human domain experts. Basically, time series are decomposed into short segments, and short-term trends of the time series within the segments (e.g., average, slope, and curvature) are described by means of polynomial models. Then, the classifiers assess short sequences of trends in subsequent segments with their rule premises. The conclusions gradually assign an input to a class. As the classifier is a generative model of the processes from which the time series are assumed to originate, anomalies can be detected, too. Segmentation and piecewise polynomial modeling are done extremely fast in only one pass over the time series. Thus, the approach is applicable to problems with harsh timing constraints. We lay the theoretical foundations for this classifier, including a new distance measure for time series and a new technique to construct a dynamic classifier from a static one, and demonstrate its properties by means of various benchmark time series, for example, Lorenz attractor time series, energy consumption in a building, or ECG data.

show abstract

Section: Discussionmentioning

confidence: 99%

SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

Fisch

Gruber

Sick

2011

IEEE Trans. Knowl. Data Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…By fusing this kind of automatically extracted knowledge with the combined, qualitative knowledge of several experts it would be possible to obtain more comprehensive knowledge about an application area. We proposed the concept of such a KD & DM technique, Collaborative Knowledge Discovery (CKD), in [2]. A CKD system not only acquires more comprehensive knowledge, but also experience (knowledge about knowledge), meaning that it is able to explain automatically extracted rules to the human experts and to assess the interestingness (e.g., novelty or utility) of these rules.…”

Section: Collaborative Knowledge Discovery and Data Miningmentioning

confidence: 99%

“…This task is very difficult: Different experts may come to different conclusions, i.e., labels. If an active data mining system is used (see, e.g., the one we described in [2]), it should be aware of its current knowledge and it should pro-actively ask human experts for their assistance. Thus, the classifier-and in particular the decision boundaries between different classes-can be iteratively refined by fusing the experts' knowledge.…”

Section: Introductionmentioning

confidence: 99%

Knowledge fusion using Dempster-Shafer theory and the imprecise Dirichlet model

Andrade

Horeis²,

Sick

2008

2008 IEEE Conference on Soft Computing in Industrial Applications

Self Cite

View full text Add to dashboard Cite

In many data mining applications, a large number of expert statements have to be fused, for example statements concerning class labels of data samples. Often, the experts do not agree, that is, they hold conflicting beliefs. There exist various combination rules that could be used to fuse such expert knowledge. We claim that, in addition to existing requirements in combination rules, the numerical evaluation of fused beliefs must reflect any persistent conflicts. We investigate three combination rules, the original Dempster-Shafer rule, Murphy's rule, and a rule based on the imprecise Dirichlet model, and show theoretically and empirically that the former two have a convergence behavior which does not comply with the new requirement. Only the rule based on the imprecise Dirichlet model exhibits the required behavior when a large number of statements is fused. We also discuss how a rule based on the imprecise Dirichlet model may be used in the fields of collaborative knowledge discovery & data mining or organic computing.

show abstract

“…Among relevant work published after CRISP-DM, particularly interesting are the RAMSYS methodology for remote collaborative KDD efforts (Moyle and Jorge, 2001), the knowledge exchange perspective of (Diamantini et al, 2006), and the knowledge fusion model of (Horeis and Sick, 2007). The ASUM-DM methodology (Haffar, 2015) of IBM is also relevant, being an augmented version of CRISP-DM.…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Model of Actors and Interactions for the Knowledge Discovery Process

Tuovinen

2016

Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

View full text Add to dashboard Cite

Abstract:The knowledge discovery process is traditionally viewed as a sequence of operations to be applied to data; the human aspect of the process is seldom taken into account, and when it is, it is mainly the roles and actions of domain and technology experts that are considered. However, non-experts can also play an important role in knowledge discovery, and furthermore, the role of technology in the process may also be substantially expanded from what it traditionally has been, with special software facilitating interactions among human actors and even operating as an actor in its own right. This diversification of the knowledge discovery process is helpful in finding tenable solutions to the new problems presented by the current deluge of digital data, but only if the process model used to manage the process adequately represents the variety of forms that the process can take. The paper addresses this requirement by presenting a conceptual model that can be used to describe different types of knowledge discovery processes in terms of the actors involved and the interactions they have with one another. Additionally, the paper discusses how the interactions can be facilitated to provide effective support for each different type of process. As a future perspective, the paper considers the implications of intelligent software taking on responsibilities traditionally reserved for human actors.

show abstract

Collaborative Knowledge Discovery & Data Mining: From Knowledge to Experience

Cited by 16 publications

References 22 publications

SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

Knowledge fusion using Dempster-Shafer theory and the imprecise Dirichlet model

A Conceptual Model of Actors and Interactions for the Knowledge Discovery Process

Contact Info

Product

Resources

About