Search results

1 – 3 of 3
Per page
102050
Citations:
Loading...
Available. Content available
Article
Publication date: 1 March 2015

Kanti V. Prasad, Kyle Ehrhardt, Yiyuan Liu and Kamlesh Tiwari

Whether older or younger entrepreneurs may be better positioned to achieve performance outcomes for their ventures is a much debated question. Here, we draw on Galenson℉s theory…

2202

Abstract

Whether older or younger entrepreneurs may be better positioned to achieve performance outcomes for their ventures is a much debated question. Here, we draw on Galenson℉s theory of creativity to propose a contingency perspective for understanding the relationship between entrepreneur age and venture performance, suggesting that a venture℉s level of innovativeness plays a moderating role. Results from a representative sample of 1,182 nascent entrepreneurs revealed mixed support for our hypotheses. While a negative relationship was found between entrepreneur age and performance for those developing “innovative” ventures, no relationship was found between entrepreneur age and performance for those developing “imitative” ventures.

Details

New England Journal of Entrepreneurship, vol. 18 no. 1
Type: Research Article
ISSN: 1550-333X

Keywords

Access Restricted. View access options
Article
Publication date: 23 August 2022

Kamlesh Kumar Pandey and Diwakar Shukla

The K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness…

120

Abstract

Purpose

The K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.

Design/methodology/approach

This study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.

Findings

The performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.

Originality/value

The KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.

Access Restricted. View access options
Article
Publication date: 19 February 2020

Himadri Majumder and Kalipada Maity

The purpose of this study aims to obtain excellent products, consistent investigation and manufacturing process control which are the preconditions that organizations have to…

112

Abstract

Purpose

The purpose of this study aims to obtain excellent products, consistent investigation and manufacturing process control which are the preconditions that organizations have to consider. Nowadays, manufacturing industry apprise process capability index (Cpi) to evaluate the nature of their things with an expect to enhance quality and also to improve the productivity by cutting down the operating cost. In this paper, process capability analysis was applied during wire electrical discharge machining (WEDM) of titanium grade 6, to study the process performance within specific limits.

Design/methodology/approach

Four machine input parameters, namely, pulse ON time, pulse OFF time, wire feed and wire tension, were chosen for process capability study. Experiments were carried out according to Taguchi’s L27 orthogonal array. The value of Cpi was evaluated for two machining attributes, namely, average surface roughness and material removal rate (MRR). For these two machining qualities, single response optimization was executed to explore the input settings, which could optimize WEDM process ability.

Findings

Optimum parameter settings for average surface roughness and MRR were found to be TON: 115 µs, TOFF: 55 µs, WF: 4 m/min and WT: 6 kgF and TON: 105 µs, TOFF: 60 µs, WF: 4 m/min and WT: 5 kgF.

Originality/value

Process capability analysis constantly checks the process quality through the capability index keep in mind the end goal to guarantee that the items made are complying with the particulars, providing data for product plan and process quality enhancement for designer and engineers, giving the support to decrease the cost of item failures.

Details

World Journal of Engineering, vol. 17 no. 1
Type: Research Article
ISSN: 1708-5284

Keywords

1 – 3 of 3
Per page
102050