Applying AI clustering to engineering tasks

Fisher, Douglas H.; Xu, Ling; Carnes, James R.; Reich, Yoram; Fenves, J.; Chen, J.; Shiavi, Richard; Biswas, Gautam; Weinberg, Jerry B.

doi:10.1109/64.248353

Cited by 27 publications

(12 citation statements)

References 6 publications

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“…Using Gennari's procedure, examination cost is not constant across nodes. 12 Carnes and Fisher (Fisher, Xu, Carnes, Reich, Fenves, Chen, Shiavi, Biswas, & Weinberg, 1993) adapted Gennari's procedure to good e ect in a diagnosis task, where the intent was to minimize the number of probes necessary to diagnose a fault. While Gennari o ers a principled focusing strategy that can be used in conjunction with an objective function, the general idea of focusing on selected features during classi cation can be traced back to Unimem (Lebowitz, 1982(Lebowitz, , 1987 and Cyrus (Kolodner, 1983).…”

Section: A Closer Look At External Validation Criteriamentioning

confidence: 99%

Iterative Optimization and Simplification of Hierarchical Clusterings

Fisher¹

1996

jair

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140

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Clustering is often used for discovering structure in data. Clustering systems di er in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a`tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modi es an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been constructed it is judged by analysts { often according to task-speci c criteria. Several authors have abstracted these criteria and posited a generic performance task akin to pattern completion, where the error rate over completed patterns is used to`externally' judge clustering utility. Given this performance task, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus promising to ease post-clustering analysis. Finally, we propose a number of objective functions, based on attribute-selection measures for decision-tree induction, that might perform well on the error rate and simplicity dimensions.

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Section: A Closer Look At External Validation Criteriamentioning

confidence: 99%

Iterative Optimization and Simplification of Hierarchical Clusterings

Fisher¹

1996

jair

Self Cite

140

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“…These activities could be realized in many ways. For example, through the use of ECOBWEB, which performs hierarchical clustering with cluster characterization (Fisher et al 1993;Reich 1993a). Clustering with characterization can also be done with traditional clustering techniques such as agglomerative hierarchical clustering (Everitt 1993;Jain et al 1999) or fuzzy clustering (Hö ppner et al 1999).…”

Section: Case Elaboration With Knowledgementioning

confidence: 99%

Case-Based Reasoning With Subjective Influence Knowledge

Reich

Kapeliuk

2004

Applied Artificial Intelligence

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Problems in domains that are highly dimensional, inhomogeneous, and context dependent are difficult to support by computational tools. If solutions to these problems must be devised based on little information that is highly subjective, the situation worsens. In this paper, we propose a new case-based reasoning (CBR) method for addressing such problems. The method is based on augmenting case descriptions with knowledge in the form of influence graphs. We use these influence graphs to cluster the space of problems. These clusters, in turn, are used to retrieve the most relevant cases given a new problem specified only by three to four attributes. We tested the system in a lab setting and found it very promising. The second contribution of the paper involves an analysis of the incorporation of knowledge in CBR. This analysis provides a basis for classifying future interactions between CBR and other knowledge sources.

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“…Any given Decision Tree is completely deterministic [11]. The decision tree nodes have some attributes, with the branches involving alternative values of those attributes.…”

Section: Some Machine Learning Algorithmsmentioning

confidence: 99%

Combining KNN and Decision Tree Algorithms to Improve Intrusion Detection System Performance

Mazinani¹,

Fathi²

2015

IJMLC

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Abstract-Two types of algorithms are realized which have been used within the supervised of model of intrusion detection systems. These algorithms are either of type eager or lazy as far as their performance is concerned. At the learning phase, the lazy algorithms are fairly simple; however, the eager algorithms are highly effective. On the other hand the classification phase is in at most contrast with learning phase. The aim of this research is, taking the advantages of both lazy and eager algorithms to achieve a hybrid algorithm. This approach necessitates employing an eager algorithm of Decision Tree, on the training set, which has led to the creation of a set of Decisions. This set of Decisions is applied on the training set, which results in having a set of binary vectors. In order to enhance the training set these binary vectors were added as new attributes. After that with the lazy algorithm of nearest neighbors, we have classified the samples. The outcome of test results from existing algorithms has been compared with our proposed algorithm. The results show that the proposed algorithm outperforms where the volume of samples are high. The performance of the hybrid algorithm is also remarkable within platforms, with limited or very high processing resources.

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Applying AI clustering to engineering tasks

Cited by 27 publications

References 6 publications

Iterative Optimization and Simplification of Hierarchical Clusterings

Iterative Optimization and Simplification of Hierarchical Clusterings

Case-Based Reasoning With Subjective Influence Knowledge

Combining KNN and Decision Tree Algorithms to Improve Intrusion Detection System Performance

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