A New Algorithm for Identifying and Quantifying of Latent Classes

Processing large amounts of data can be greatly simplified if this data is divided into approximately homogeneous groups. Splitting into such groups is the task of cluster analysis. However, the question of constructing an objective, natural partition into clusters remains open. The paper considers a modern approach to the search for such an objective cluster structure by highlighting the indicator of a common essential part from the set of characteristics that define objects (we call them the forming ones). When this indicator is fixed, the remains of the forming characteristics become independent or close to such. The resulting independent residuals are interpreted as a kind of information noise, and the latent cluster variable, the common fixed part that provides such a transformation, can be a reason for the objective integration of objects into clusters. A new algorithm for the formation of a cluster partition based on the proximity or coincidence of the values of a latent cluster variable with the simultaneous quantification of its values is proposed. The algorithm is based on the targeted search of partitions, the transition from the start one to the partition, more close to the objective. The algorithm proposed in the paper can be easily modified to the case of non-numeric categorized characteristics.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.