An Enhanced Spectral Clustering Algorithm with S-Distance

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

Are Innovation Output and Economic Output Strongly Related in Emerging Industrial Clusters? Evidence from China

For many countries, innovation-driven development has become a prevalent consensus because innovation can effectively stimulate economic growth. Emerging industries are innovation-intensive with high potential economic benefit. However, is it assured that high innovation output means high economic benefit? In October of 2010, China State Council initiated the Decision of Speeding up Cultivation and Development of Strategic Emerging Industries, signifying top-down policy mobilization to advance emerging industries. According to seven types of emerging industries defined in the Decision, we collected data from official industrial databases to figure out spatial divergence of emerging industries in terms of innovation output and economic benefit over the years from 2000 to 2011. We construct twodimension scatter diagrams based on number of granted patents as the indicator of innovation output and industrial locational quotient as the indicator of industrial economic benefit. The result shows that China has seen preliminary spatial clustering of key emerging industries across regions and industries in the light of innovation output and economic benefit. However, not all regions with high innovation output have high economic benefit. The spatial divergence is closely related to region-specific and industry-specific characteristics. We offer policy implications to facilitate targeted emerging industries with more detailed policy and regional endowment.

Selected problems of designing modern industrial databases

The paper presents problems of designing databases for various branches of industry. The development of information technologies and in particular of object-oriented programming has caused a change from data modelling to the modelling of applications. The increase of unstructured Big Data in Industry 4.0 era and requirements of sharing data model between many applications needs a reversion to data analysis and design and it is presented in the article.

Evolutionary Computing

The field of evolutionary computation forms one of the tenets of the soft computing paradigm, which aims at deriving at some possible global optimal solutions to search problems. The field of industrial informatics, being an emergent field, faces tremendous data explosion and associated challenges of data redundancies and inconsistencies. Different evolutionary algorithms have been put to use to evolve intelligence out of redundancies immanent in industrial databases. Industrial portfolio management has been a much-talked affair nowadays, thanks to the evolving fields of data intelligent management and archival techniques. An overview of the different facets of evolutionary algorithms and their role in imbibing human intelligence in data management and retrieval is presented with regards to its application in the optimization of a collection of financial portfolio instruments.