Production and non-productive equipment and personnel delays are a critical element of any production system. The frequency and length of delays impact heavily on the production and economic efficiency of these systems. Machining processes in wood industry are particularly vulnerable to productive and non-productive delays. Whereas, traditional manufacturing industries usually operate on homoge...

Data clustering is a common technique for data analysis. It is used in many fields including machine learning, data mining, customer segmentation, trend analysis, pattern recognition and image analysis. The proposed Localized Diffusion Folders (LDF) methodology, whose localized folders are called diffusion folders (DF), introduces consistency criteria for hierarchical folder organization, clust...

Information about the state and planning of the speaker is obscured in traditional classifications of disfluencies which are generally at the word level. This study delves into the acoustic and prosodic information of repetitions, one of the most common disfluencies. A hierarchical clustering of prosodic features reveals three subsets of repetitions, each reflecting different problems in planning.

We create a weighted lexical network derived from the cosine similarities of financial news feeds to compare two clustering methods, Newman's Modularity method and hierarchical clustering. We find that hierarchical clustering, clustering documents according to shared unique terms, shows results that are closer to expectation.

Clustering data has been of great interest to many researchers. Hierarchical clustering methods have been preferred because clusters can be visualized as a dendrogram. One of the problems of hierarchical clustering methods, however, is that the resulting dendrogram is not visually pleasing due to the scaling problem. Hence, a series of iterated logarithmic function is proposed so as to mitigate...

In the hierarchical clustering algorithms, it has become a basic difficult problem to determine the optimal clustering number in the dataset, as a result of the influence of outliers and noise points. Therefore, we propose a method to remove these interferential data in two stages in the hierarchical clustering algorithm, which is based on the traditional noise data removal method. Furthermore,...

The Urban spatial structure is affected by spatial interactions among various activity locations, and land uses in the city over the transportation system. Each city has its unique circulation pattern of passengers and freight due to its unique geographic conditions and the distribution of locations of economic activities. In that sense, it is claimed in this chapter per the authors that urban ...

Hierarchical clustering algorithm of cluster analysis method can detect data in different granularity levels, and the clustering process is suitable for most of practical problems, but complexity of the merger and split conditions limits its application. This paper proposed a new hierarchical clustering algorithm basis on dynamic fuzzy granular computing combining with the particle swarm optimi...