Robust Clustering Using Discriminant Analysis

Bhatnagar, Vasudha; Ahuja, Sangeeta

doi:10.1007/978-3-642-14400-4_11

Cited by 3 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We experimented the dataset with RCDA cluster ensemble algorithm [15] for K (number of clusters = 5) with varying the number of partitions (H = 2, 4, 6, 8, 10, 12, 14, 16 and 18) respectively. Here, we get the optimum partition H = 8, because at this value of partition, we obtained the lowest value of SSE (Sum of Squared Error) and maximum (improved) clustering quality.…”

Section: Results With 5 Clustersmentioning

confidence: 99%

“…So, we eliminated the three noisy tuples and also it can be seen from the Figure 4 that last six bins (numbered (11,12,13,15), 14, 16) consists of only 0, 1, 2 and 3 tuples respectively. So, we eliminated the six noisy tuples.…”

Section: Regionalization Using Rcdamentioning

confidence: 99%

“…RCDA [15] is a recent algorithm for generating a robust clustering scheme using discriminant analysis. Robust Clustering Using Discriminant Analysis (RCDA) algorithm takes H partitions as input with K clusters in each partition and delivers a robust partition with same number of clusters, and noise, if any.…”

Section: Rcda (Robust Clustering Usingmentioning

confidence: 99%

See 2 more Smart Citations

Regionalization of River Basins Using Cluster Ensemble

Ahuja¹

2012

JWARP

Self Cite

View full text Add to dashboard Cite

In the wake of global water scarcity, forecasting of water quantity and quality, regionalization of river basins has attracted serious attention of the hydrology researchers. It has become an important area of research to enhance the quality of prediction of yield in river basins. In this paper, we analyzed the data of Godavari basin, and regionalize it using a cluster ensemble method. Cluster Ensemble methods are commonly used to enhance the quality of clustering by combining multiple clustering schemes to produce a more robust scheme delivering similar homogeneous basins. The goal is to identify, analyse and describe hydrologically similar catchments using cluster analysis. Clustering has been done using RCDA cluster ensemble algorithm, which is based on discriminant analysis. The algorithm takes H base clustering schemes each with K clusters, obtained by any clustering method, as input and constructs discriminant function for each one of them. Subsequently, all the data tuples are predicted using H discriminant functions for cluster membership. Tuples with consistent predictions are assigned to the clusters, while tuples with inconsistent predictions are analyzed further and either assigned to clusters or declared as noise. Clustering results of RCDA algorithm have been compared with Best of k-means and Clue cluster ensemble of R software using traditional clustering quality measures. Further, domain knowledge based comparison has also been performed. All the results are encouraging and indicate better regionalization of the Godavari basin data.

show abstract

Section: Results With 5 Clustersmentioning

confidence: 99%

Section: Regionalization Using Rcdamentioning

confidence: 99%

See 1 more Smart Citation

Regionalization of River Basins Using Cluster Ensemble

Ahuja¹

2012

JWARP

Self Cite

View full text Add to dashboard Cite

show abstract

“…RCDA [19] is a recent algorithm for generating a robust clustering scheme using discriminant analysis. Robust Clustering Using Discriminant Analysis (RCDA) algorithm takes H partitions as input with K clusters in each partition and delivers a robust partition with same number of clusters, and noise, if any.…”

Section: Rcda (Robust Clustering Usingmentioning

confidence: 99%

Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India

Ahuja¹,

Dhanya

2012

JSEA

Self Cite

View full text Add to dashboard Cite

The magnitude and frequency of precipitation is of great significance in the field of hydrologic and hydraulic design and has wide applications in varied areas. However, the availability of precipitation data is limited to a few areas, where the rain gauges are successfully and efficiently installed. The magnitude and frequency of precipitation in ungauged sites can be assessed by grouping areas with similar characteristics. The procedure of grouping of areas having similar behaviour is termed as Regionalization. In this paper, RCDA cluster ensemble algorithm is employed to identify the homogeneous regions of rainfall in India. Cluster ensemble methods are commonly used to enhance the quality of clustering by combining multiple clustering schemes to produce a more robust scheme delivering similar homogeneous regions. The goal is to identify, analyse and describe hydrologically similar regions using RCDA cluster ensemble algorithm. RCDA cluster ensemble algorithm, which is based on discriminant analysis. The algorithm takes H base clustering schemes each with K clusters, obtained by any clustering method, as input and constructs discriminant function for each one of them. Subsequently, all the data tuples are predicted using H discriminant functions for cluster membership. Tuples with consistent predictions are assigned to the clusters, while tuples with inconsistent predictions are analyzed further and either assigned to clusters or declared as noise. RCDA algorithm has been compared with Best of K-means and Clue cluster ensemble of R software using traditional clustering quality measures. Further, domain knowledge based comparison has also been performed. All the results are encouraging and indicate better regionalization of the rainfall in different parts of India.

show abstract

“…A suitable clustering algorithm and parameter settings vary from the individual input and expected results. Numerous attempts were made to improve the quality of clusters using ensembling techniques [5] [6] [7] [8] [9] [10] [11] [12]. The main concern of many of these algorithms is to elucidate label correspondence problem.…”

Section: Introductionmentioning

confidence: 99%

Clustering in Aggregated User Profiles Across Multiple Social Networks

Virmani¹,

Pillai²,

Juneja³

2017

IJECE

View full text Add to dashboard Cite

A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble K-means clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.

show abstract

Robust Clustering Using Discriminant Analysis

Cited by 3 publications

References 12 publications

Regionalization of River Basins Using Cluster Ensemble

Regionalization of River Basins Using Cluster Ensemble

Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India

Clustering in Aggregated User Profiles Across Multiple Social Networks

Contact Info

Product

Resources

About