Regular pattern mining on multidimensional databases

Extracting general patterns from a multidimensional database is a tricky task. Designing an algorithm to seek the frequency or no. of occurring patterns and really first-class transaction dimension of a mining pattern, general patterns from a multidimensional database is the objective of the task. Analysis prior to mining required patterns from database hence, Apriori algorithm is used. After the acquiring patterns, they have been improved to many further patterns. Nevertheless, to mine the required patterns from a multidimensional database we use FP development algorithm. Here, now we have carried out a pop-growth procedure to mine fashionable patterns from multidimensional database established on their repute values. Utilizing this opportunity, we studied about recognizing patterns which give the reputation of every object or movements inside the entire database. Whereas Apriori and FP-growth algorithm is determined by the aid or frequency measure of an object set. As a result, to acquire required patterns utilizing these programs one has to mine FP-growth tree recursively which involves extra time consumption. We have utilized a mining process, which is meant for multidimensional recognized patterns. It overcomes the limitations of present mining ways by implementing lazy pruning method followed by showing downward closure property.

Automating statistical diagrammatic representations with data characterization

The search for an efficient method to enhance data cognition is especially important when managing data from multidimensional databases. Open data policies have dramatically increased not only the volume of data available to the public, but also the need to automate the translation of data into efficient graphical representations. Graphic automation involves producing an algorithm that necessarily contains inputs derived from the type of data. A set of rules are then applied to combine the input variables and produce a graphical representation. Automated systems, however, fail to provide an efficient graphical representation because they only consider either a one-dimensional characterization of variables, which leads to an overwhelmingly large number of available solutions, a compositional algebra that leads to a single solution, or requires the user to predetermine the graphical representation. Therefore, we propose a multidimensional characterization of statistical variables that when complemented with a catalog of graphical representations that match any single combination, presents the user with a more specific set of suitable graphical representations to choose from. Cognitive studies can then determine the most efficient perceptual procedures to further shorten the path to the most efficient graphical representations. The examples used herein are limited to graphical representations with three variables given that the number of combinations increases drastically as the number of selected variables increases.

OLAP and machine learning

The objective of this tutorial is to present an overview of machine learning (ML) methods. This paper outlines different types of ML as well as techniques for each kind. It covers popular applications for different types of ML. On-Line Analytic Processing (OLAP) enables users of multidimensional databases to create online comparative summaries of data. This paper goes over commercial OLAP software available as well as OLAP techniques such as “slice and dice” and “drill down and roll up.” It discusses various techniques and metrics used to evaluate how accurate a ML algorithm is.