3DM: Domain-oriented Data-driven Data Mining

Abstract

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledge mind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics[1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.

Big Data Analytics Has Little to Do with Analytics

“…Literature such as such as CRISP-DM [2] and Domain-oriented data mining [3] is advocating the importance of considering practices related to analytics and establishing good understanding of data to build better analytic solutions more effectively. Significant limitations observed in data analytic solution engineering space are a lack of high-level architectural and data models to understand how to compose analytic pipelines, how data should flow between the different stages and how to create mappings between the stages and appropriate tools and data sets in the underlying infrastructure.…”

Granular computing with multiple granular layers for brain big data processing

“…In the perspective of knowledge transformation [ 53 ], the process of data analyzing and problem solving by fuzzy sets or rough sets is actually to find a mapping from the information represented by the original finest-grained data to the knowledge hidden behind a set of optimized coarser and more abstract IGs.…”

The Determination Of Patient Profile At Pamukkale University As Relater To The Application Of Data Mining

“…Bilgisayar biliminin yeni bir dalı olan veri madenciliği son yıllarda çok fazla ilgiye sahiptir [7]. Veri tabanı sistemlerinin artan kullanımı ve veri depolama ünitelerinin hacimlerindeki artış sorgulama ve raporlama araçlarının dev veri yığınları karşısında etkisiz kalmasına yol açmıştır.…”