Foundation For Data Mining Research Articles

A multidimensional data warehouse design to combat the health pandemics

The Covid-19 pandemic has brought about a new lifestyle for across the globe. Throughout this period, the use of holistic methods has become indispensable to deal with the enormous amount of data in this regard. It appears that the simplest way to tackle this issue is to spread the digitalization efforts concerning all data-based applications. Given the significance of pandemic data management, it is essential to have a data warehouse that collects, associates, and communicates these data. Containing a significant volume of structured data, warehousing can provide the necessary foundation for data mining and the development of analytical tools. To this end, the present paper proposes a data warehouse for combatting and managing pandemics, with the possibility to be enhanced for other personal or public health-related initiatives. In this research, the bottom-up data warehouse building methodology is used to construct a warehouse. A fact constellation schema model is utilized to accommodate the information ranging from citizen demographics to physician-prescribed drugs and laboratory tests. Sample queries are executed based on the proposed data warehouse for different purposes, and desired query results are obtained within proper response times. The proposed data warehouse contributes to countrywide implementation of pandemic practices and illuminates research on faster, less expensive, and safer management of citywide, nationwide, or worldwide health emergencies within a robust technical framework by governments.

Analytics of Specialized Data by Data Mining

In this paper, we are trying to discover Predictive Analytics by means of combining special information mining methods with large data. Predictive lookup consists of a number of mathematical and analytical techniques to increase new techniques for doable prediction possibilities. The paper also portrays the integration of Big Data characteristics as the foundation of Data Mining by Apache Hadoop's usefulness in achieving the above. With the resource of accessible statistics mining techniques, predictive analytics predicts the activities in future and can make tips referred to as prescriptive analytics. This evaluates paper offers clear thought to follow facts mining strategies and predictive analytics on distinctive clinical dataset to predict a variety of ailments with accuracy levels, execs and cons that conclude about the troubles of these algorithms and futuristic processes on huge data.

Expressive power of an algebra for data mining

The relational data model has simple and clear foundations on which significant theoretical and systems research has flourished. By contrast, most research on data mining has focused on algorithmic issues. A major open question is: what's an appropriate foundation for data mining, which can accommodate disparate mining tasks? We address this problem by presenting a database model and an algebra for data mining. The database model is based on the 3W-model introduced by Johnson et al. [2000]. This model relied on black box mining operators. A main contribution of this article is to open up these black boxes, by using generic operators in a data mining algebra. Two key operators in this algebra are regionize , which creates regions (or models) from data tuples, and a restricted form of looping called mining loop . Then the resulting data mining algebra MA is studied and properties concerning expressive power and complexity are established. We present results in three directions: (1) expressiveness of the mining algebra; (2) relations with alternative frameworks, and (3) interactions between regionize and mining loop.

Conceptual construction on incomplete survey data

The raw survey data for data mining are often incomplete. The issues of missing data in knowledge discovery are often ignored in data mining. This article presents the conceptual foundations of data mining with incomplete survey data, and proposes query processing for knowledge discovery and a set of query functions for the conceptual construction in survey data mining. Through a case, this paper demonstrates that conceptual construction on incomplete data can be accomplished by using artificial intelligence tools such as self-organizing maps.