Positional and confidence voting-based consensus functions for fuzzy cluster ensembles

Sevillano, Xavier; Álías, Francesc; Socoró, Joan Claudi

doi:10.1016/j.fss.2011.09.007

Cited by 22 publications

(9 citation statements)

References 15 publications

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“…Clustering is the process of partitioning a set of data into meaningful similar subclasses called clusters. Many real-world application domains require fuzzy clustering, such as the application of positional and confidence voting techniques [21], handling very large data [22] and patient stratification [23].…”

Section: ) Cosine Distance Methodmentioning

confidence: 99%

Context-aware-based Location Recommendation for Elderly Care

Kurnianingsih¹,

Nugroho²,

Widyawan³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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As adults age, the body declines. Living independently at home can be a significant challenge for the elderly, particularly for those who suffer from dementia or who have memory impairment. Assisting the elderly to live independently and safely in their own homes by providing appropriate services for them and ensuring that caregivers are immediately alerted in the event of an emergency is crucial. Utilizing context in the recommendation process will make recommendations more appropriate. A model of a context-aware-based location recommender system that can seamlessly monitor the location of the elderly and deliver appropriate location recommendations by considering context is proposed as our contribution. Two scenarios are investigated: (1) we classify location as follows: bedroom (class 1), dining room (class 2), and living room (class 3); (2) we classify location as follows: inside (class 1) and outside (class 2) the bedroom. We evaluate our proposed model using a distance measure concept by employing the cosine distance method. We compare the cosine distance method with fuzzy inference system (FIS) rules on labeled data. The results of the experiments for the first scenario show that the cosine distance has better average accuracy than the fuzzy inference system. For the second scenario, fuzzy c-means (FCM) has the same average accuracy as cosine distance. FCM has slightly better accuracy in class 1 compared to cosine distance (1% difference in accuracy), whereas cosine distance has slightly better accuracy in class 2 compared to the FCM (1% difference in accuracy). In general, we can draw the conclusion that, on this dataset, cosine distance which uses a simple algorithm produced better results than the fuzzy inference system which uses a more complex algorithm.

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Section: ) Cosine Distance Methodmentioning

confidence: 99%

Context-aware-based Location Recommendation for Elderly Care

Kurnianingsih¹,

Nugroho²,

Widyawan³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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“…Its use in machine learning and computational intelligence is very frequent. Let us only mention some of related work, e.g., the regression ensemble in [15], clustering ensembles in [16,17] or classification ensembles [18,19].…”

Section: Remarkmentioning

confidence: 99%

Fuzzy rule base ensemble generated from data by linguistic associations mining

Štěpnička

Burda

Štěpničková

2016

Fuzzy Sets and Systems

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“…The improvement make GK can be used to estimate grouping whether the variables are linearly correlated or not. Second weakness, the unstable result can be solved by using the ensemble approach [9]. The ensemble approach in clustering is performed by combining some of the results of clustering to produce a stable and robust cluster through consensus techniques [10].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Based Gustafson Kessel Fuzzy Clustering

Firmansyah¹,

Pramana²

2018

J. Data Sci. Appl.

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Fuzzy clustering is a clustering method whcih allows an object to belong to two or more cluster by combining hard-clustering and fuzzy membership matrix. Two popular algorithms used in fuzzy clustering are Fuzzy C-Means (FCM) and Gustafson Kessel (GK). The FCM use Euclideans distance for determining cluster membership, while GK use Fuzzy Covariance Matrix that considering covariance between variables. Although GK perform better, it has some drawbacks on handling linearly correlated data, and as FCM the algorithm produce unstable result due to random initialization. These drawbacks can be overcame by using improved covariance estimation and cluster ensemble, respectively. This research presents the implementation of improved covariance estimation and cluster ensemble on GK method and compare it with FCM-Ensemble.

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Positional and confidence voting-based consensus functions for fuzzy cluster ensembles

Cited by 22 publications

References 15 publications

Context-aware-based Location Recommendation for Elderly Care

Context-aware-based Location Recommendation for Elderly Care

Fuzzy rule base ensemble generated from data by linguistic associations mining

Ensemble Based Gustafson Kessel Fuzzy Clustering

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