Context-Based Distance Learning for Categorical Data Clustering

Ienco, Dino; Pensa, Ruggero G.; Meo, Rosa

doi:10.1007/978-3-642-03915-7_8

Cited by 60 publications

(91 citation statements)

References 7 publications

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“…All these steps are addressed in a fully unsupervised scenario where no class information is available differently from many previous strategies that work in supervised context [1]. To deal with the first point we exploit the method proposed in [12], named DILCA. This method is able to extract numerical statistics from a set of categorical examples in order to summarize the underlying data distribution.…”

Section: Methodsmentioning

confidence: 99%

Change detection in categorical evolving data streams

Ienco

Bifet

Pfahringer

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution.To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream.

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Section: Methodsmentioning

confidence: 99%

Change detection in categorical evolving data streams

Ienco

Bifet

Pfahringer

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

Self Cite

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“…Another K-Mode Algorithm is DILCA Algorithm [12] proposed a method called Distance learning for Categorical Attributes. The distance between two values of a categorical attribute was determined by the way in which the value of the other attributes was distributed in the dataset.…”

Section: Literature Surveymentioning

confidence: 99%

Comparative Study of Initial Centroid Based K-Mode Algorithm

2017

IJAERD

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“…The value of symmetrical uncertainty ranges between 0 and 1. The value of 1 indicates that one variable (either X or Y) completely predicts the other variable [19] .The value of 0 indicates that both variables are completely independent.…”

Section: A Dependency Measuresmentioning

confidence: 99%

A Rank Aggregation Algorithm for Ensemble of Multiple Feature Selection Techniques in Credit Risk Evaluation

Dahiya¹,

Handa²,

Singh³

2016

ijarai

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Abstract-In credit risk evaluation the accuracy of a classifier is very significant for classifying the high-risk loan applicants correctly. Feature selection is one way of improving the accuracy of a classifier. It provides the classifier with important and relevant features for model development. This study uses the ensemble of multiple feature ranking techniques for feature selection of credit data. It uses five individual rank based feature selection methods. It proposes a novel rank aggregation algorithm for combining the ranks of the individual feature selection methods of the ensemble. This algorithm uses the rank order along with the rank score of the features in the ranked list of each feature selection method for rank aggregation. The ensemble of multiple feature selection techniques uses the novel rank aggregation algorithm and selects the relevant features using the 80%, 60%, 40% and 20% thresholds from the top of the aggregated ranked list for building the C4.5, MLP, C4.5 based Bagging and MLP based Bagging models. It was observed that the performance of models using the ensemble of multiple feature selection techniques is better than the performance of 5 individual rank based feature selection methods. The average performance of all the models was observed as best for the ensemble of feature selection techniques at 60% threshold. Also, the bagging based models outperformed the individual models most significantly for the 60% threshold. This increase in performance is more significant from the fact that the number of features were reduced by 40% for building the highest performing models. This reduces the data dimensions and hence the overall data size phenomenally for model building. The use of the ensemble of feature selection techniques using the novel aggregation algorithm provided more accurate models which are simpler, faster and easy to interpret.

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Context-Based Distance Learning for Categorical Data Clustering

Cited by 60 publications

References 7 publications

Change detection in categorical evolving data streams

Change detection in categorical evolving data streams

Comparative Study of Initial Centroid Based K-Mode Algorithm

A Rank Aggregation Algorithm for Ensemble of Multiple Feature Selection Techniques in Credit Risk Evaluation

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