Discovering optimal clusters using firefly algorithm

Mohammed, Athraa Jasim; Yusof, Yuhanis; Husni, Husniza

doi:10.1504/ijdmmm.2016.081239

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 5 and 6 are initialization of first two centers so their complexity is O (1). Then, remaining K-2 centers are computed in step 8, traversing through entire data set making the loop run (n * (K-2)) times.…”

Section: B Methods -Ii: Far Efficient K-means (Fekm)mentioning

confidence: 99%

See 1 more Smart Citation

Efficient Intelligent Framework for Selection of Initial Cluster Centers

Mishra¹,

Rath²,

Nanda³

et al. 2019

IJISA

View full text Add to dashboard Cite

At present majority of research is on cluster analysis which is based on information retrieval from data that portrays the objects and their association among them. When there is a talk on good cluster formation, then selection of an optimal cluster core or center is the necessary criteria. This is because an inefficient center may result in unpredicted outcomes. Hence, a sincere attempt had been made to offer few suggestions for discovering the near optimal cluster centers. We have looked at few versatile approaches of data clustering like K-Means, TLBOC, FEKM, FECA and MCKM which differs in their initial center selection procedure. They have been implemented on diverse data sets and their inter and intra cluster formation efficiency were tested using different validity indices. The clustering accuracy was also conducted using Rand index criteria. All the algorithms computational complexity was analyzed and finally their computation time was also recorded. As expected, mostly FECA and to some extend FEKM and MCKM confers better clustering results as compared to K-Means and TLBOC as the former ones manages to obtain near optimal cluster centers. More specifically, the accuracy percentage of FECA is higher than the other techniques however, it's computational complexity and running time is moderately higher.

show abstract

Section: B Methods -Ii: Far Efficient K-means (Fekm)mentioning

confidence: 99%

“…-Ref. [1]‖ proposed WFA_selection, a modified weight-based firefly selection algorithm for obtaining optimal clusters. This algorithm merges a group of selected clusters to produce clusters of better superiority.…”

Section: Related Workmentioning

confidence: 99%

Efficient Intelligent Framework for Selection of Initial Cluster Centers

Mishra¹,

Rath²,

Nanda³

et al. 2019

IJISA

View full text Add to dashboard Cite

show abstract

“…However, it’s only limitation is, it needs to have prior information of the number of clusters to be created. Mohammed et al 10 introduced WFA selection, a modified weight-based firefly selection algorithm designed to attain optimal clusters. This algorithm amalgamates a selection of clusters to generate clusters of superior quality.…”

Section: Introductionmentioning

confidence: 99%

An efficient framework for obtaining the initial cluster centers

Mishra,

Mohanty,

Baidyanath

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Clustering is an important tool for data mining since it can determine key patterns without any prior supervisory information. The initial selection of cluster centers plays a key role in the ultimate effect of clustering. More often researchers adopt the random approach for this purpose in an urge to get the centers in no time for speeding up their model. However, by doing this they sacrifice the true essence of subgroup formation and in numerous occasions ends up in achieving malicious clustering. Due to this reason we were inclined towards suggesting a qualitative approach for obtaining the initial cluster centers and also focused on attaining the well-separated clusters. Our initial contributions were an alteration to the classical K-Means algorithm in an attempt to obtain the near-optimal cluster centers. Few fresh approaches were earlier suggested by us namely, far efficient K-means (FEKM), modified center K-means (MCKM) and modified FEKM using Quickhull (MFQ) which resulted in producing the factual centers leading to excellent clusters formation. K-means, which randomly selects the centers, seem to meet its convergence slightly earlier than these methods, which is the latter’s only weakness. An incessant study was continued in this regard to minimize the computational efficiency of our methods and we came up with farthest leap center selection (FLCS). All these methods were thoroughly analyzed by considering the clustering effectiveness, correctness, homogeneity, completeness, complexity and their actual execution time of convergence. For this reason performance indices like Dunn’s Index, Davies–Bouldin’s Index, and silhouette coefficient were used, for correctness Rand measure was used, for homogeneity and completeness V-measure was used. Experimental results on versatile real world datasets, taken from UCI repository, suggested that both FEKM and FLCS obtain well-separated centers while the later converges earlier.

show abstract

High-Dimensional Text Datasets Clustering Algorithm Based on Cuckoo Search and Latent Semantic Indexing

Boushaki

Kamel

Bendjeghaba

2018

J. Info. Know. Mgmt.

View full text Add to dashboard Cite

The clustering is an important data analysis technique. However, clustering high-dimensional data like documents needs more effort in order to extract the richness relevant information hidden in the multidimensionality space. Recently, document clustering algorithms based on metaheuristics have demonstrated their efficiency to explore the search area and to achieve the global best solution rather than the local one. However, most of these algorithms are not practical and suffer from some limitations, including the requirement of the knowledge of the number of clusters in advance, they are neither incremental nor extensible and the documents are indexed by high-dimensional and sparse matrix. In order to overcome these limitations, we propose in this paper, a new dynamic and incremental approach (CS_LSI) for document clustering based on the recent cuckoo search (CS) optimization and latent semantic indexing (LSI). Conducted Experiments on four well-known high-dimensional text datasets show the efficiency of LSI model to reduce the dimensionality space with more precision and less computational time. Also, the proposed CS_LSI determines the number of clusters automatically by employing a new proposed index, focused on significant distance measure. This later is also used in the incremental mode and to detect the outlier documents by maintaining a more coherent clusters. Furthermore, comparison with conventional document clustering algorithms shows the superiority of CS_LSI to achieve a high quality of clustering.

show abstract

Discovering optimal clusters using firefly algorithm

Cited by 3 publications

References 0 publications

Efficient Intelligent Framework for Selection of Initial Cluster Centers

Efficient Intelligent Framework for Selection of Initial Cluster Centers

An efficient framework for obtaining the initial cluster centers

High-Dimensional Text Datasets Clustering Algorithm Based on Cuckoo Search and Latent Semantic Indexing

Contact Info

Product

Resources

About