Bipartite Graphs for Monitoring Clusters Transitions

Market segmentation analysis does not end with the selection of the target segment, and the development of a customised marketing mix. As Lilien and Rangaswamy (2003, p. 103) state segmentation must be viewed as an ongoing strategic decision process. Haley (1985, p. 261) elaborates as follows: The world changes . . . virtually the only practical option for an intelligent marketer is to monitor his or her market continuously. After the segmentation strategy is implemented, two additional tasks need to be performed on an ongoing basis:1. The effectiveness of the segmentation strategy needs to be evaluated. Much effort goes into conducting the market segmentation analysis, and customising the marketing mix to best satisfy the target segment's needs. These efforts should result in an increase in profit, or an increase in achievement of the organisational mission. If they did not, the market segmentation strategy failed. 2. The market is not static. Consumers change, the environment, and actions of competitors change. As a consequence, a process of ongoing monitoring of the market segmentation strategy must be devised. This monitoring process can range from a regular review by the segmentation team, to a highly automatised data mining system alerting the organisation to any relevant changes to the size or nature of the target segment. Evaluating the Success of the Segmentation StrategyThe aim of evaluating the effectiveness of the market segmentation strategy is to determine whether developing a customised marketing mix for one or more segments did achieve the expected benefits for the organisation. In the short term,

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“…A similar approach is used by Oliveira and Gama (2010). In their framework, the following taxonomy is used for changes in segments over time:…”

Section: Segment Evolutionmentioning

confidence: 99%

Step 10: Evaluation and Monitoring

Dolničar

Grün

Leisch

2018

Management for Professionals

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“…MONIC [1] and MClusT/MEC [5] define clusters as set of objects. Therefore, structural changes is defined based on overlap of cluster members between two clusters of two time periods.…”

Section: Related Workmentioning

confidence: 99%

“…For example, MONIC [1] defines changes based on cluster member overlap, and MClusT/MEC [5] defines changes based on the conditional probability of cluster members that migrate to another cluster. Defining changes based on cluster membership alone cannot detect the movement of a cluster centroid when the members of the cluster remain unchanged.…”

Section: Introductionmentioning

confidence: 99%

Relative density estimation using Self-Organizing Maps

Denny

2014

2014 International Conference on Advanced Computer Science and Information System

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Organizations need knowledge of change, such as changes in customer purchasing behaviour, to adapt business strategies in response to changing circumstances. To understand what has changed, analysts have to be able to relate new knowledge acquired from a newer dataset to that acquired from an earlier dataset. This paper presents a method to detect changes in clustering structure over time. Discovering clustering changes can also be applied in other contexts, such as fraud detection and customer attrition analysis.The key contribution of this paper is the enhancement of the measurement of relative density using SOM. This measurement is used in the visualization method called Relative Density Self-Organizing Map (ReDSOM) to compare clustering structures from two snapshot datasets. This visualization provide means for analysts to visually identify and analyze various changes in the clustering structure, such as emerging clusters, disappearing clusters, splitting clusters, and merging clusters.These contributions have been evaluated using synthetic datasets, as well as real-life datasets from the World Bank. Experiments showed that the new measure is more sensitive in detecting changes in density.

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“…An example is the study of graphs obtained by considering nodes as communities and links between them as an indicator of overlap between communities. This strategy is used by Oliveira and Gama (2010) for the detection of transitions in clusters.…”

Section: Community-level Trajectoriesmentioning

confidence: 99%

Visualization of evolving social networks using actor‐level and community‐level trajectories

Oliveira¹,

Gama²

2013

Expert Systems

Self Cite

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In recent years we witnessed an impressive advance in the social networks field, which became a "hot" topic and a focus of considerable attention. Also, the development of methods that focus on the analysis and understanding of the evolution of data are gaining momentum. In this paper we present an approach to visualize the evolution of dynamic social networks by using Tucker decomposition and the concept of temporal trajectory. Our visualization strategy is based on the definition of trajectories, both at the node-level and at the community-level, in a bidimensional space that preserves its structural properties. Furthermore, this approach can be used to identify similar actors, or communities of actors, by comparing the shape and position of trajectories. To illustrate the proposed approach we conduct a case study using a set of temporal friendship networks.

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Bipartite Graphs for Monitoring Clusters Transitions

Cited by 29 publications

References 19 publications

Step 10: Evaluation and Monitoring

Step 10: Evaluation and Monitoring

Relative density estimation using Self-Organizing Maps

Visualization of evolving social networks using actor‐level and community‐level trajectories

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