scholarly journals Applying Soft Cluster Analysis Techniques to Customer Interaction Information

2010 ◽  
pp. 49-78
Author(s):  
Randall E. Duran ◽  
Li Zhang ◽  
Tom Hayhurst
Keyword(s):  
Cluster Analysis ◽  
Analysis Techniques ◽  
Interaction Information ◽  
Customer Interaction ◽  
Soft Cluster

scholarly journals Discrimination of nano-objects via cluster analysis techniques applied to time-resolved thermo-acoustic microscopy

Ultrasonics ◽  
2021 ◽  
Vol 114 ◽  
pp. 106403
Author(s):  
Andrea Ronchi ◽  
Andrea Sterzi ◽  
Marco Gandolfi ◽  
Ali Belarouci ◽  
Claudio Giannetti ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Acoustic Microscopy ◽  
Time Resolved ◽  
Analysis Techniques

scholarly journals A Computerized Text and Cluster Analysis Approach to Psychotherapy Talk

2020 ◽  
Vol 9 (1) ◽  
pp. 4-25
Author(s):  
Dennis Tay
Keyword(s):  
Cluster Analysis ◽  
Agglomerative Hierarchical Clustering ◽  
Analytic Thinking ◽  
Future Directions ◽  
Analysis Techniques ◽  
Entry Points ◽  
And Cluster Analysis ◽  
Language Styles ◽  
Computer Text ◽  
Qualitative Exploration

This paper illustrates an analytical approach combining LIWC, a computer text-analytic application, with cluster analysis techniques to explore ‘language styles’ in psychotherapy across sessions in time. It categorizes session transcripts into distinct clusters or styles based on linguistic (di)similarity and relates them to sessional progression, thus providing entry points for further qualitative exploration. In the first step, transcripts of four illustrative therapist-client dyads were scored under ten LIWC variables including ‘analytic thinking’, ‘clout’, ‘authenticity’, ‘emotional tone’, and pronoun types. In the next step, agglomerative hierarchical clustering uncovered distinct session clusters that are differently distributed in each dyad. The relationships between these clusters and the chronological progression of sessions were then further discussed in context as contrastive exemplars. Applications, limitations and future directions are highlighted.

Cluster Analysis Using Rough Clustering and k-Means Clustering

2005 ◽  
pp. 435-438
Author(s):  
Kevin E. Voges
Keyword(s):  
Cluster Analysis ◽  
Data Reduction ◽  
Computational Intelligence ◽  
Rough Sets ◽  
Information Science ◽  
Reduction Technique ◽  
Analysis Techniques ◽  
The Subject ◽  
Data Reduction Technique ◽  
Potential Use

Cluster analysis is a fundamental data reduction technique used in the physical and social sciences. The technique is of interest to managers in information science because of its potential use in identifying user needs though segmenting users such as Web site visitors. In addition, the theory of rough sets is the subject of intense interest in computational intelligence research. The extension of this theory into rough clustering provides an important and potentially useful addition to the range of cluster analysis techniques available to the manager.

scholarly journals Analysis of Multiple-Angle Microwave Observations of Snow and Ice Using Cluster-Analysis Techniques

1981 ◽  
Vol 27 (95) ◽  
pp. 89-97 ◽  
Author(s):  
Stanley R. Rotman ◽  
Arthur D. Fisher ◽  
David H. Staelin
Keyword(s):  
Cluster Analysis ◽  
Sea Ice ◽  
Accumulation Rate ◽  
Ice Cover ◽  
Ice Age ◽  
Analysis Techniques ◽  
Brightness Temperatures ◽  
Microwave Spectrometer ◽  
Angular Data ◽  
Microwave Signatures

AbstractThe Nimbus-6 satellite carries the Scanning Microwave Spectrometer experiment (SCAMS) which continuously maps the Earth’s surface at two frequencies (22.235 and 31.65 GHz) and at six angles besides nadir. Cluster analysis was applied to these observations to determine the influence of various geophysical parameters on the radiometric brightness temperatures.Characteristic microwave signatures for a variety of terrain were obtained by this method; discrete clusters were distinguished for sea ice (with sub-classes for ice age and fractional ice cover) and firn (with accumulation-rate sub-classes). The availability of the angular data greatly facilitated separate determinations of the extent of continuous sea ice and mixtures of sea ice and water.

Weed community structure in spring-seeded crops in Manitoba

1991 ◽  
Vol 71 (4) ◽  
pp. 1069-1080 ◽  
Author(s):  
A. G. Thomas ◽  
M. R. T. Dale
Keyword(s):  
Cluster Analysis ◽  
Community Structure ◽  
Weed Ecology ◽  
Analysis Techniques ◽  
Temperature And Precipitation ◽  
Weed Communities ◽  
Geographic Regions ◽  
Production Practices ◽  
Using Data ◽  
And Cluster Analysis

The phytosociological structure of weed communities in spring wheat, barley, oats, flax, and canola was investigated using data collected during a 3-yr survey of 1384 fields in Manitoba. Fields were surveyed during July and August, after the application of all herbicides. Association and cluster analysis techniques, using the presence or absence of species in a field, were employed to distinguish co-occurring groups of species. Only a small number of significant positive and negative associations were found between species and only minor clusters with a few species were formed at low similarity levels. These results indicated that the weed community was composed of species responding to conditions more or less independently of each other. A comparison of weed associations among the five crops and four geographic regions in the province indicated that the weed community structure was determined largely by climatic variables. The pattern of weed association in the four geographic regions was correlated with differences in temperature and precipitation during the spring and summer. The lack of floristic differentiation was attributed to the fact that production practices were similar for the five spring-seeded crops. Key words: Weed communities, weed ecology, cluster analysis, association analysis

Measuring Cluster Similarity across Methods

2000 ◽  
Vol 86 (3) ◽  
pp. 858-862 ◽  
Author(s):  
Anthony J. Kos ◽  
Clement Psenicka
Keyword(s):  
Cluster Analysis ◽  
Cluster Structure ◽  
Hypergeometric Distribution ◽  
Clustering Techniques ◽  
Cluster Analyses ◽  
Analysis Techniques ◽  
Important Measure

Cluster analysis techniques delineate groupings or categories of observations based on some shared commonality over a set of variables. If such groupings can be formed, their commonality may be investigated to define relationships that may otherwise go undetected given their complexity. However, the cluster analyses are inappropriate unless the results can be replicated. A number of clustering techniques are available, differing mostly in the technical criteria used to judge the similarity of the observations. There is added validity to the cluster structure when different methods produce similar groupings however, in most cases, different clustering techniques will not produce identical clusters and the extent of cluster similarity becomes an important measure. In this paper the hypergeometric distribution is used to gauge cluster similarity across different methods, providing an appropriate measure of consistency. This measure is used to validate reproducibility of the clusters.

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