Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes

A key issue in cluster analysis is the choice of an appropriate clustering method and the determination of the best number of clusters. Different clusterings are optimal on the same data set according to different criteria, and the choice of such criteria depends on the context and aim of clustering. Therefore, researchers need to consider what data analytic characteristics the clusters they are aiming at are supposed to have, among others within-cluster homogeneity, between-clusters separation, and stability. Here, a set of internal clustering validity indexes measuring different aspects of clustering quality is proposed, including some indexes from the literature. Users can choose the indexes that are relevant in the application at hand. In order to measure the overall quality of a clustering (for comparing clusterings from different methods and/or different numbers of clusters), the index values are calibrated for aggregation. Calibration is relative to a set of random clusterings on the same data. Two specific aggregated indexes are proposed and compared with existing indexes on simulated and real data.Comment: 42 pages, 11 figure

Energy transition pathways amongst low-income urban households: A mixed method clustering approach.

Studies on clean energy transition amongst low-income urban households in the Global South use an array of qualitative and quantitative methods. However, attempts to combine qualitative and quantitative methods are rare and there are a lack of systematic approaches to this. This paper demonstrates a two stage approach using clustering methods to analyse a mixed dataset containing quantitative household survey data and qualitative interview data. By clustering the quantitative and qualitative data separately, latent groups with common characteristics and narratives arising from each of the two analyses are identified. A second stage of clustering identifies links between these qualitative and quantitative clusters and enables inference of energy transition pathways followed by low-income urban households defined by both quantitative characteristics and qualitative narratives. This approach can support interdisciplinary collaboration in energy research, providing a systematic approach to comparing and identifying links between quantitative and qualitative findings.•A mixed dataset comprising of quantitative survey data and qualitative interview data on low-income household energy use is analysed using hierarchical clustering to detect communities within each dataset.•Interviewees are matched to quantitative survey clusters and a second stage of clustering is performed using cluster membership as variables.•Second stage clusters identify common pairs of survey and interview clusters which define energy transition pathways based on socio-economic characteristics, energy use patterns, and narratives for decision making and practices

INSAM Journal of Contemporary Music, Art and Technology 2

The subject of machine learning and creativity, as well as its appropriation in arts is the focus of this issue with our Main theme of – Artificial Intelligence in Music, Arts, and Theory. In our invitation to collaborators, we discussed our standing preoccupation with the exploration of technology in contemporary theory and artistic practice. The invitation also noted that this time we are encouraged and inspired by Catherine Malabou’s new observations regarding brain plasticity and the metamorphosis of (natural and artificial) intelligence. Revising her previous stance that the difference between brain plasticity and computational architecture is not authentic and grounded, Malabou admits in her new book, Métamorphoses de l'intelligence: Que faire de leur cerveau bleu? (2017), that plasticity – the potential of neuron architecture to be shaped by environment, habits, and education – can also be a feature of artificial intelligence. “The future of artificial intelligence,” she writes, “is biological.” We wanted to provoke a debate about what machines can learn and what we can learn from them, especially regarding contemporary art practices. On this note, I am happy to see that our proposition has provoked intriguing and unique responses from various different disciplines including: theory of art, aesthetics of music, musicology, and media studies. The pieces in the (Inter)view section deal with machine and computational creativity, as well as the some of the principles of contemporary art. Reviews give us an insight into a couple of relevant reading points for this discussion and a retrospective of one engaging festival that also fits this theme