Clustering algorithms allow data to be grouped according to their inherent characteristics. There are many algorithms that have been developed over the years. Among the most famous ones, we cannot forget DBSCAN. Let’s find out, step by step, how this method succeeds in identifying clusters of data of any shape and size thanks to only two parameters.
Elasticsearch offers the ability to extract statistics and data sets using aggregation functions. In many contexts, however, it is necessary to concatenate the results of these analyses to obtain more refined results. Let us see how aggregation pipelines allow us to obtain the results of our interest.
With Elasticsearch’s bucket aggregations we can create groups of documents. Having seen in the previous article aggregations based on keyword type fields, we will now focus on other functions oriented to other data types. In particular, we will use aggregations for definitions of numeric ranges, dates, or groups based on georeferenced data.
With Elasticsearch’s bucket aggregations we can create groups of documents. In this article we will mainly focus on aggregations based on keyword type fields in indexes. We will use several examples to understand the main differences between the available aggregation functions.
In addition to text search, Elasticsearch allows analysis on data using aggregations. Among the various types of aggregation available, the metric ones are aimed precisely at calculating statistics on one or more fields. Through examples we will see what information we can extract with this type of aggregation.
Elasticsearch is a widely used NoSQL database for developing search engines because of its ability to index text appropriately. But it does not stop at just that. Thanks to aggregations, Elasticsearch can be used to analyze data and extract statistics from large masses of data. Let’s learn about this functionality of his that underlies many visualizations used by Kibana.