Integrating Apriori with paired k-means for Cluster fixed mixed data

Haripriya, H.; Amrutha, S; Veena, R.; Nedungadi, Prema

doi:10.1145/2791405.2791437

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Except the algorithms mentioned in the introduction, there are some other approaches to handle mixed types of variables. These include categorizing all continuous variables (Haripriya et al, 2015) or converting categorical variables into continuous or dummy variables and then treat the dummy variables as continuous (Hennig and Liao, 2013). However, both ideas will lead to information loss.…”

Section: Clustering Algorithms For Mixed Types Of Variablesmentioning

confidence: 99%

Hybrid Density- and Partition-Based Clustering Algorithm for Data With Mixed-Type Variables

Wang¹,

Yabes²,

Chang³

2021

Journal of Data Science

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Clustering is an essential technique for discovering patterns in data. Many clustering algorithms have been developed to tackle the ever increasing quantity and complexity of data, yet algorithms that can cluster data with mixed variables (continuous and categorical) remain limited despite the abundance of mixed-type data. Of the existing clustering methods for mixed data types, some posit unverifiable distributional assumptions or rest on unbalanced contributions of different variable types. To address these issues, we propose a two-step hybrid density-and partitionbased (HyDaP) algorithm to detect clusters after variable selection. The first step involves both density-based and partition-based algorithms to identify the data structure formed by continuous variables and determine important variables (both continuous and categorical) for clustering. The second step involves a partition-based algorithm together with our proposed novel dissimilarity measure to obtain clustering results. Simulations across various scenarios were conducted to compare the HyDaP algorithm with other commonly used methods. Our HyDaP algorithm was applied to identify sepsis phenotypes and yielded important results.

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Section: Clustering Algorithms For Mixed Types Of Variablesmentioning

confidence: 99%

Hybrid Density- and Partition-Based Clustering Algorithm for Data With Mixed-Type Variables

Wang¹,

Yabes²,

Chang³

2021

Journal of Data Science

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“…Joining step is used to generate a candidate set of two item sets and the prune step involves reducing the size as it will help to avoid heavy computations. In the joining step, it will list all the items and its support value followed by the pruning step, which will prune the items with minimal support value [3]. Apriori algorithm supports to obtain frequent itemsets completely.…”

Section: Literature Surveymentioning

confidence: 99%

“…The field of data mining is progressing very rapidly as many and new algorithms that are faster are being introduced every day. Each of these new algorithms is more or less based on the Apriori algorithm or its variations [3] (Table 1).…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on Associative Rule Mining Technique for Multi-label Classification (ARM-MLC)

Prathibhamol

Ananthakrishnan

Nandan

et al. 2020

Advances in Intelligent Systems and Computing

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In this paper, we have implemented an efficient and novel technique for multi-label class prediction using associative rule mining. Many of the research works for the classification have been carried out on single-label datasets, but it is not useful for all real-world application accounting to multi-label datasets like scene classification, text categorization, etc. Hence, we propose an algorithm for performing multi-label classification and solve the problems which come across in the domain pertaining to single-label classification. Our novel technique (ARM-MLC) will aim in enhancing the accuracy of any decision-making processes. Here, in multi-label classification, based on our work, we aim to predict the multiple characters of the instances.

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“…There are some other approaches to handle mixed types of variables. These include categorizing all continuous variables (Haripriya et al, 2015) or converting categorical variables into continuous or dummy variables and then treat the dummy variables as continuous (Hennig and Liao, 2013).…”

Section: Model-based Clusteringmentioning

confidence: 99%

Hybrid Density- and Partition-based Clustering Algorithm for Data with Mixed-type Variables

Wang,

Yabes,

Chang

2019

Preprint

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Clustering is an essential technique for discovering patterns in data. The steady increase in amount and complexity of data over the years led to improvements and development of new clustering algorithms. However, algorithms that can cluster data with mixed variable types (continuous and categorical) remain limited, despite the abundance of data with mixed types particularly in the medical field. Among existing methods for mixed data, some posit unverifiable distributional assumptions or that the contributions of different variable types are not well balanced.We propose a two-step hybrid density-and partition-based algorithm (HyDaP) that can detect clusters after variables selection. The first step involves both density-based and partitionbased algorithms to identify the data structure formed by continuous variables and recognize the important variables for clustering; the second step involves partition-based algorithm together with a novel dissimilarity measure we designed for mixed data to obtain clustering results. Simulations across various scenarios and data structures were conducted to examine the performance of the HyDaP algorithm compared to commonly used methods. We also applied the HyDaP algorithm on electronic health records to identify sepsis phenotypes.

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Integrating Apriori with paired k-means for Cluster fixed mixed data

Cited by 4 publications

References 13 publications

Hybrid Density- and Partition-Based Clustering Algorithm for Data With Mixed-Type Variables

Hybrid Density- and Partition-Based Clustering Algorithm for Data With Mixed-Type Variables

A Novel Approach Based on Associative Rule Mining Technique for Multi-label Classification (ARM-MLC)

Hybrid Density- and Partition-based Clustering Algorithm for Data with Mixed-type Variables

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