## Data Mining with Fuzzy Methods: Status and Perspectives (1999)

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Venue: | In: Proceedings of the EUFIT’99 |

Citations: | 3 - 0 self |

### BibTeX

@INPROCEEDINGS{Kruse99datamining,

author = {Rudolf Kruse and Detlef Nauck and Christian Borgelt},

title = {Data Mining with Fuzzy Methods: Status and Perspectives},

booktitle = {In: Proceedings of the EUFIT’99},

year = {1999}

}

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### Abstract

: Data mining is the central step in a process called knowledge discovery in databases, namely the step in which modeling techniques are applied. Several research areas like statistics, artificial intelligence, machine learning, and soft computing have contributed to its arsenal of methods. In this paper, however, we focus on fuzzy methods for rule learning, information fusion, and dependency analysis. In our opinion fuzzy approaches can play an important role in data mining, because they provide comprehensible results (although this goal is often neglected---maybe because it is sometimes hard to achieve with other methods). In addition, the approaches studied in data mining have mainly been oriented at highly structured and precise data. However, we expect that the analysis of more complex heterogeneous information source like texts, images, rule bases etc. will become more important in the near future. Therefore we give an outlook on information mining, which we see as an extension o...

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Citation Context ...a, has to be taken into account. For this reason learning possibilistic networks from data is a noteworthy alternative, the theory of which can be developed in close analogy to the probabilistic case =-=[15, 16, 4, 5, 22]-=-. These methods can be used to do dependency analysis, even if the data to analyze is highly imprecise and thus offer interesting perspectives for future research. 5 Perspective: Information Mining Al... |

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Citation Context ...a, has to be taken into account. For this reason learning possibilistic networks from data is a noteworthy alternative, the theory of which can be developed in close analogy to the probabilistic case =-=[15, 16, 4, 5, 22]-=-. These methods can be used to do dependency analysis, even if the data to analyze is highly imprecise and thus offer interesting perspectives for future research. 5 Perspective: Information Mining Al... |

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Citation Context ...e graphs, and Markov networks [26], which are based on undirected graphs, are the most prominent examples. However, this approach has also been generalized to be usable with other uncertainty calculi =-=[32]-=-, for instance, in the so-called valuation-based networks [33] and has been implemented, for example, in PULCINELLA [31]. Due to their connection to fuzzy systems, which in the past have successfully ... |

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Citation Context ...s obtained using fuzzy approaches are easy to understand and to apply. Due to these strengths, fuzzy systems are the method of choice, if linguistic, vague, or imprecise information has to be modeled =-=[25]-=-. Data mining is concerned with the analysis of large but homogeneous data. Our information-oriented world, however, demands that we also automate the analysis of more complex information sources like... |

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Citation Context ...r levels, where raw data is involved, the term (sensor) data fusion is preferred. For a conceptual and comparative study of fusion strategies for expert opinions in various calculi of uncertainty see =-=[10, 12, 17]-=-. NEFCLASS and neuro-fuzzy systems in general can be used to integrate expert knowledge in form of fuzzy rules and information obtained from data. If prior knowledge about the classification problem i... |

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Citation Context ...and imprecision are involved---decomposition techniques, that reduce the reasoning process to computations in lower-dimensional subspaces, have become very popular. In the field of graphical modeling =-=[23, 36]-=-, decomposition is based on dependence and independence relations between the attributes or variables that are used to describe the domain under consideration. The structure of these dependence and in... |

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Citation Context ... cluster analysis [3, 21], i.e., the learning process is unsupervised. Hyperbox-oriented approaches use a supervised learning algorithm that tries to cover the training data by overlapping hyperboxes =-=[2]-=-. Fuzzy rules are created in both approaches by projection of clusters or hyperboxes. The main problem of both approaches is that each generated fuzzy rule uses individual membership functions and thu... |

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Citation Context ...a, has to be taken into account. For this reason learning possibilistic networks from data is a noteworthy alternative, the theory of which can be developed in close analogy to the probabilistic case =-=[15, 16, 4, 5, 22]-=-. These methods can be used to do dependency analysis, even if the data to analyze is highly imprecise and thus offer interesting perspectives for future research. 5 Perspective: Information Mining Al... |

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Citation Context ...r levels, where raw data is involved, the term (sensor) data fusion is preferred. For a conceptual and comparative study of fusion strategies for expert opinions in various calculi of uncertainty see =-=[10, 12, 17]-=-. NEFCLASS and neuro-fuzzy systems in general can be used to integrate expert knowledge in form of fuzzy rules and information obtained from data. If prior knowledge about the classification problem i... |

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Citation Context ...rules by descending performances values. For an application in the context of stock prediction that is based on another neuro-fuzzy approach, which is implemented in the neural network tool SENN, see =-=[34]-=-. The decision on that option depends on the trust we have in the experts knowledge and in the training data. A mixed approach can be used, e.g. include the best expert rules and then use the best lea... |

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Citation Context ...tain degree, thus modeling the penumbra of the linguistic term describing the property that defines the set [24]. Interpretations of membership degrees include similarity, preference, and uncertainty =-=[11]-=-: They can state how similar an object or case is to a prototypical one, they can indicate preferences between suboptimal solutions to a problem, or they can model uncertainty about the true situation... |

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Citation Context ...wo classes. The first class, fuzzy data analysis, consists of approaches that analyze fuzzy data---data derived from imprecise measurement instruments or from the descriptions of human domain experts =-=[6]-=-. An example from our own research is the induction of possibilistic graphical models from data which complements the induction of the well-known probabilistic graphical models. The second class, fuzz... |

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