K-Means over Incomplete Datasets Using Mean Euclidean Distance

Abdallah, Loai; Shimshoni, Ilan

doi:10.1007/978-3-319-41920-6_9

Cited by 9 publications

(15 citation statements)

References 10 publications

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“…Based on the derived formulas, the MD E distance and the mean, our aim in this research is to develop k-means clustering algorithms for incomplete datasets [1].…”

Section: K-means Clustering Using the MD E Distancementioning

confidence: 99%

“…In this research, we developed two popular clustering algorithms to run over incomplete datasets: (1) k-means clustering algorithm [1] and (2) mean shift clustering algorithms [2].…”

Section: Introductionmentioning

confidence: 99%

“…There are two basic types of methods to deal with the problem of incomplete datasets. (1) Deletion: methods from this category ignore all the incomplete instances. These methods may change the distribution of the data by decreasing the volume of the dataset [7].…”

Section: Introductionmentioning

confidence: 99%

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Clustering Algorithms for Incomplete Datasets

Abdallah¹,

Shimshoni²

2018

Recent Applications in Data Clustering

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Many real-world dataset suffers from the problem of missing values. Several methods were developed to deal with this problem. Many of them filled the missing values within fixed value based on statistical computation. In this research, we developed a new versions of the k-means and the mean shift clustering algorithms that deal with datasets with missing values without filling their values. We developed a new distance function that is able to compute distances over incomplete datasets. The distance was computed based only on the mean and variance of the data for each attribute. As a result, the runtime complexity of our computation was O 1 ðÞ. We experimented on six standard numerical datasets from different fields. On these datasets, we simulated missing values and compared the performance of the developed algorithms using our distance and the suggested mean computations to other three basic methods. Our experiments show that the developed algorithms using our distance function outperform the existing k-means and mean shift using other methods for dealing with missing values.

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“…Based on the derived formulas, the MD E distance and the mean, our aim in this research is to develop k-means clustering algorithms for incomplete datasets [1].…”

Section: K-means Clustering Using the MD E Distancementioning

confidence: 99%

“…In this research, we developed two popular clustering algorithms to run over incomplete datasets: (1) k-means clustering algorithm [1] and (2) mean shift clustering algorithms [2].…”

Section: Introductionmentioning

confidence: 99%

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Clustering Algorithms for Incomplete Datasets

Abdallah¹,

Shimshoni²

2018

Recent Applications in Data Clustering

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“…In consequence, we need to run clustering algorithms that can deal with datasets of this type. As a result, we decided to use the k-means clustering algorithm (Abdallah and Shimshoni, 2016) and the mean shift clustering algorithm (Abdallah and Shimshoni, 2014), designed to cluster incomplete datasets in order to build the cluster matrix.…”

Section: 2mentioning

confidence: 99%

“…But since this research is for incomplete data, we use only clustering algorithms that are able to cluster incomplete datasets. In our experiments we decided to work with the mean shift clustering algorithm (Abdallah and Shimshoni, 2014) and the k-means clustering algorithm (Abdallah and Shimshoni, 2016), which were developed to deal with missing values.…”

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confidence: 99%

Lookahead selective sampling for incomplete data

Abdallah

Shimshoni

2016

International Journal of Applied Mathematics and Computer Science

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Missing values in data are common in real world applications. There are several methods that deal with this problem. In this paper we present lookahead selective sampling (LSS) algorithms for datasets with missing values. We developed two versions of selective sampling. The first one integrates a distance function that can measure the similarity between pairs of incomplete points within the framework of the LSS algorithm. The second algorithm uses ensemble clustering in order to represent the data in a cluster matrix without missing values and then run the LSS algorithm based on the ensemble clustering instance space (LSS-EC). To construct the cluster matrix, we use the k-means and mean shift clustering algorithms especially modified to deal with incomplete datasets. We tested our algorithms on six standard numerical datasets from different fields. On these datasets we simulated missing values and compared the performance of the LSS and LSS-EC algorithms for incomplete data to two other basic methods. Our experiments show that the suggested selective sampling algorithms outperform the other methods.

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