Douglas H. Fisher. Knowledge Acquisition Via Incremental Conceptual Clustering. Machine Learning, 2(2):139--172, 1987. Home/Search Document Not in Database Summary Related Articles Check |
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Feature Selection for Clustering - A Filter Solution - Dash, Choi, Scheuermann, Liu (Correct)
....decide the optimal number of features. In [9, 18] features are ranked and selected for categorical data. Forward and backward search techniques are used to generate candidate subsets. To evaluate each candidate subset, these methods measure the category utility of the clusters by applying COBWEB [12]. In [20] authors proposed an objective function for choosing the feature subset and finding the optimal number of clusters for a document clustering problem using a Bayesian statistical estimation framework. Examples of local wrapper methods are [1, 2, 6] Projected clustering (ProClus [1] ....
D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139--172, 1987.
An Algorithm for Non-distance Based Clustering in High.. - Zhu, Li (2002) (Correct)
....al. 1998; Ng and Han, 1994; Ester et al. 1995a; Ester et al. 1995c; Ester et al. 1995b; Li et al. 2001] statistics [Brito et al. 1997; Berger and Rigoutsos, 1991; Duda and Hart, 1973; Dubes and Jain, 1980; Lee, 1981; Murtagh, 1983] and machine learning communities [Cheeseman et al. 1988; Fisher, 1987; Fisher, 1995; Lebowitz, 1987; Liu et al. 2000] with di erent approaches and di erent focuses. The clustering problem can be described as follows: let W be a set of n multi dimensional data points, we want to nd a partition of W into clusters such that the points within each cluster are ....
Douglas H. Fisher, \Knowledge Acquisition via Incremental Conceptal Clustering, " Machine Learning, 2(2), 1987.
Learning Implicit User Interest Hierarchy for Context in - Personalization Hyoung Kim (Correct)
....two children in the hierarchy. Divisive (top down) hierarchical clustering algorithms are similar to agglomerative ones, except that initially all objects start in one cluster which is repeatedly split. Splits are usually binary and one usual stopping criterion is the desired number of clusters [4]. Our divisive algorithm does not necessarily generate binary splits and uses a minimum cluster size as one of the stopping criteria. Partitioning clustering algorithms such as the K means algorithm initially create a partitioning of K clusters. Those initial K clusters are then iteratively ....
....connected words, but the K means algorithm divides whole words into K clusters without removing weak relations. COBWEB is a incremental conceptual clustering algorithm. Each cluster records the probability of each attribute and value, and the probabilities are updated every time an object is added [4]. However, instead of using category utility to determine if child clusters are generated, we use a graph based method and a different similarity function. To build user interest profiles that can be used for web personalization, Richardson and Domingos [11] enhanced PageRank by using a more ....
Fisher, D.H. Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning 2, 139-172, 1987.
Batch Learning of Disjoint Feature Intervals - Akkus (1996) (1 citation) (Correct)
....learning tasks, the most commonly used metric is the percentage of correctly classified instances over all test instances. This metric cannot be used for unsupervised learning tasks like conceptual clustering, but this measure can be generalized as the average ability to predict attribute values [23]. Accuracy of an algorithm is a measure of correct classifications on a test set of unseen instances. There are several ways of measuring the accuracy of an algorithm, in the literature the common techniques are cross validation, leave one out and average of randomized runs. Cross Validation: In ....
D.H. Fisher, Knowledge Acquisition Via Incremental Conceptual Clustering, Machine Learning, 2:139-172, 1987.
Non-Incremental Classification Learning Algorithms Based On.. - Demiröz (Correct)
....tasks, the most commonly used metric is the percentage of correctly classified test instances over all test instances. This metric cannot be used for unsupervised learning tasks like conceptual clustering, but this measure can be generalized as the average ability to predict attribute values [26]. Accuracy of an algorithm is a measure of correct classifications on a test set of unseen instances. There are several ways of measuring the accuracy of an algorithm, in the literature the common techniques are cross validation, leave one out and average of randomized runs. N fold ....
....the human expert. But the next highest vote, received by Feature values of test instance 1: F[1] 2 F[2] 3 F[3] 3 F[4] 3 F[5] 3 F[6] 0 F[7] 0 F[8] 0 F[9] 3 F[10] 3 F[11] 0 F[12] 0 F[13] 0 F[14] 0 F[15] 0 F[16] 0 F[17] 3 F[18] 2 F[19] 2 F[20] 3 F[21] 3 F[22] 3 F[23] 1 F[24] 3 F[25] 0 F[26]:0 F[27] 0 F[28] 0 F[29] 0 F[30] 0 F[31] 0 F[32] 1 F[33] 0 F[34] 34 Classes: 1] 2] 3] 4] 5] 6] Votes of Feature[1] 0.16 0.16 0.17 0.18 0.15 0.18 Votes of Feature[2] 0.36 0.27 0.15 0.07( 0.05( 0.10 Votes of ....
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D.H. Fisher, Knowledge Acquisition Via Incremental Conceptual Clustering, Machine Learning, 2:139--172, 1987.
A Multi-Agent System for E-Insurance Brokering - Nogueira, Oliveira (2002) (Correct)
....methods, raging from statistical clustering techniques to neural networks and symbolic machine learning. The branch of symbolic machine learning that deals with this kind of unsupervised learning is called conceptual clustering and a popular representative of this approach is the cobweb algorithm [4]. Conceptual clustering is a type of learning by observation that is particularly suitable for summarising and explaining data. Summarisation is achieved through the discovery of appropriate clusters, which involve determining useful subsets of an object set. In unsupervised learning each example ....
D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139172, 1987.
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Douglas H. Fisher. Knowledge Acquisition Via Incremental Conceptual Clustering. Machine Learning, 2(2):139--172, 1987.
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D. Fisher: "Knowledge Acquisition via Incremental Conceptual Clustering". Machine Learning, Vol.2, 139-172, 1987.
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FISHER D.H 1987. Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning 2, 139-172.
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D.H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139--172, 1987. 23
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D. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139--172, 1987.
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D. H. Fisher. 1987. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2(2):139--172.
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FISHER D. H., "Knowledge Acquisition via Incremental Conceptual Clustering", Machine Learning, vol. 2, 1987, p. 139--172, Kluwer Academic Publishers, Boston.
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D. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139--172, 1987.
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Fisher D. (1987). Knowledge Acquisition Via Incremental Conceptual Clustering. In: Michalski, R.S., Carbonell, J., Mitchell, T.(eds.): Machine Learning: An Artificial Intelligence Approach. San Mateo, CA, Morgan Kaufmann. II, pp. 139-172.
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