K-modes Clustering Algorithm for Categorical Data

Sharma, Neha; Gaud, Nirmal

doi:10.5120/ijca2015906708

Cited by 21 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another paper referred to as "K-modes cluster algorithmic rule for Categorical Data" by N. Sharma and N. Gaud [2], gave ample data relating to the formulas and mathematical approaches of K-modes algorithmic rule. Moreover, it explained K-modes being associate extension to the quality K-Means cluster algorithmic rule and shows the most modifications to K-Means additionally.…”

Section: Literature Surveymentioning

confidence: 99%

Untitled

2021

International Advanced Research Journal in Science, Engineering

View full text Add to dashboard Cite

Customer segmentation permits for an efficient allocation of promoting resources and maximizes opportunities to cross-sell and up-sell clients so as to extend average order price & customer period price. In today's era of availability of wide variety of products and services, customer segmentation and identifying the potential customer aids in effective business growth. This paper shows the grouping of the customers, using the k-means algorithm, based on the RFM (Recency Frequency Monetary) analysis followed by the customer life time value prediction to identify the potential customers.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Untitled

2021

International Advanced Research Journal in Science, Engineering

View full text Add to dashboard Cite

show abstract

“…Most popular clustering algorithms typically follow one of the following two principles: (1) principle of maximisation of inter-cluster distances and minimisation of intra-cluster distances [9], and (2) principle of density-based clustering [10,11]. The most popular algorithms that work on inter-cluster and intra-cluster distance include K-means clustering (KMC) [12] and Kohonen self-organising map (SOM) [13,14]. In contrast, based on density-based clustering principle, clusters are dense regions in the data space, separated by regions of lower object density.…”

Section: Requirement Two: Cluster Detectionmentioning

confidence: 99%

OCEAN: A Non-Conventional Parameter Free Clustering Algorithm Using Relative Densities of Categories

Gheyas

Parkinson

Khan

2020

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

In this paper, we propose a fully autonomous density-based clustering algorithm named ‘Ocean’, which is inspired by the oceanic landscape and phenomena that occur in it. Ocean is an improvement over conventional algorithms regarding both distance metric and the clustering mechanism. Ocean defines the distance between two categories as the difference in the relative densities of categories. Unlike existing approaches, Ocean neither assigns the same distance to all pairs of categories, nor assigns arbitrary weights to matches and mismatches between categories that can lead to clustering errors. Ocean uses density ratios of adjacent regions in multidimensional space to detect the edges of the clusters. Ocean is robust against clusters of identical patterns. Unlike conventional approaches, Ocean neither makes any assumption regarding the data distribution within clusters, nor requires tuning of free parameters. Empirical evaluations demonstrate improved performance of Ocean over existing approaches.

show abstract

“…As an extension of k-means, the k-modes clustering algorithm was introduced to deal with categorical data [16,17]. K-modes replaces the cluster means with modes, and uses a frequency-based method to update modes in the k-means manner, which removes the numerical data limitation of k-means while maintaining its efficiency [20].…”

Section: Partitioning Clustering Algorithmmentioning

confidence: 99%

Public Preferences and Willingness to Pay for Shared Autonomous Vehicles Services in Nagoya, Japan

Hao

Yamamoto

2019

Smart Cities

View full text Add to dashboard Cite

Shared autonomous vehicle systems are anticipated to offer cleaner, safer, and cheaper mobility services when autonomous vehicles are finally implemented on the roads. The evaluation of people’s intentions regarding shared autonomous vehicle services appears to be critical prior to the promotion of this emerging mobility on demand approach. Based on a stated preference survey in Nagoya, Japan, the preference for shared autonomous vehicle services as well as willingness to pay for these services were examined among 1036 respondents in order to understand the relationship between people’s socioeconomic characteristics and their preferred shared autonomous vehicle services. For this purpose, k-modes clustering technique was selected and six clusters were obtained. Six groups with respect to different interests on shared autonomous vehicle services were clustered. The result of correlation analysis and discussion of willingness to pay on services provided insightful results for the future shared autonomous vehicle services. This study not only aids in revealing the demands of customer different clusters, but also states the prospective needs of users for stakeholders from research, policymaker and industry field, who are preparing to work on promoting shared autonomous vehicle systems, and subsequently, develops an optimum transportation mode by considering both demand and services as a whole.

show abstract

K-modes Clustering Algorithm for Categorical Data

Cited by 21 publications

References 6 publications

Untitled

Untitled

OCEAN: A Non-Conventional Parameter Free Clustering Algorithm Using Relative Densities of Categories

Public Preferences and Willingness to Pay for Shared Autonomous Vehicles Services in Nagoya, Japan

Contact Info

Product

Resources

About