Dynamic Feature Selection in Incremental Hierarchical Clustering

Talavera, Luis

doi:10.1007/3-540-45164-1_40

Cited by 31 publications

(34 citation statements)

References 11 publications

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“…Other related work has been done on feature selection as a pre-processing step for hierarchical clustering algorithms (Talavera, 1999). However, feature selection algorithms simply select a subset of meaningful features from V , by setting degrees of relevance to either zero or one.…”

Section: Introductionmentioning

confidence: 99%

Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm

Amorim

2015

J Classif

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In this paper we introduce a new hierarchical clustering algorithm called Ward p . Unlike the original Ward, Ward p generates feature weights, which can be seen as feature rescaling factors thanks to the use of the L p norm. The feature weights are cluster dependent, allowing a feature to have different degrees of relevance at different clusters.We validate our method by performing experiments on a total of 75 realworld and synthetic datasets, with and without added features made of uniformly random noise. Our experiments show that: (i) the use of our feature weighting method produces results that are superior to those produced by the original Ward method on datasets containing noise features; (ii) it is indeed possible to estimate a good exponent p under a totally unsupervised framework. The clusterings produced by Ward p are dependent on p. This makes the estimation of a good value for this exponent a requirement for this algorithm, and indeed for any other also based on the L p norm.

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Section: Introductionmentioning

confidence: 99%

Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm

Amorim

2015

J Classif

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“…Filter approaches use an evaluation function based on the characteristics of all data, independently of any classification algorithm [6][7][8][9][10]. These methods are fast, general and less expensive in computation time, which allows them to operate more easily with databases of very large dimensions.…”

Section: Introductionmentioning

confidence: 99%

Validation Study of Dimensionality Reduction Impact on Breast Cancer Classification

Hamdi¹,

Auhmani²,

Hassani³

2015

IJCSIT

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A fundamental problem in machine learning is identifying the most representative subset of features from which we can construct a predictive model for a classification task. This paper aims to present a validation study of dimensionality reduction effect on the classification accuracy of mammographic images. The studied dimensionality reduction methods were: locality-preserving projection (LPP), locally linear embedding (LLE), Isometric Mapping (ISOMAP) and spectral regression (SR). We have achieved high rates of classifications. In some combinations the classification rate was 100%. But in most of the cases the classification rate is about 95%. It was also found that the classification rate increases with the size of the reduced space and the optimal value of space dimension is 60. We proceeded to validate the obtained results by measuring some validation indices such as: Xie-Beni index, Dun index and Alternative Dunindex. The measurement of these indices confirms that the optimal value of reduced space dimension is d=60.

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“…In case of unsupervised classification it is very difficult to measure the goodness of a particular feature. In recent years some works have been reported to solve the unsupervised feature selection problem (Dash and Liu 1999;Dy and Brodley 2004;Kim et al 2002;Mitra et al 2002;Peña et al 2001;Talavera 1999). But most of these techniques pose the feature selection problem as a single objective optimization technique.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Semi-supervised Clustering Using Multiobjective Optimization

Alok

Saha

Ekbal

2014

2014 International Conference on Soft Computing and Machine Intelligence

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In this paper we have coupled feature selection problem with semi-supervised clustering. Semi-supervised clustering utilizes the information of unsupervised and supervised learning in order to overcome the problems related to them. But in general all the features present in the data set may not be important for clustering purpose. Thus appropriate selection of features from the set of all features is very much relevant from clustering point of view. In this paper we have solved the problem of automatic feature selection and semi-supervised clustering using multiobjective optimization. A recently created simulated annealing based multiobjective optimization technique titled archived multiobjective simulated annealing (AMOSA) is used as the underlying optimization technique. Here features and cluster centers are encoded in the form of a string. We assume that for each data set for 10% data points class level information are known to us. Two internal cluster validity indices reflecting different data properties, an external cluster validity index measuring the similarity between the obtained partitioning and the true labelling for 10% data points and a measure counting the number of features present in a particular string are optimized using the search capability of AMOSA. AMOSA is utilized to detect the appropriate subset of features, appropriate number of clusters as well as the appropriate partitioning from any given data set. The effectiveness of the proposed semi-supervised feature selection technique as compared to the existing techniques is shown for seven real-life data sets of varying complexities.

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Dynamic Feature Selection in Incremental Hierarchical Clustering

Cited by 31 publications

References 11 publications

Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm

Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm

Validation Study of Dimensionality Reduction Impact on Breast Cancer Classification

Feature Selection and Semi-supervised Clustering Using Multiobjective Optimization

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