Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types

DATA MINING TECHNOLOGIES

Knowledge discovery and data mining software (Knowledge Discovery and Data Mining - KDD) as an interdisciplinary field emersion have been in rapid growth to merge databases, statistics, industries closely related to the desire to extract valuable information and knowledge in a volume as possible.There is a difference in understanding of "knowledge discovery" and "data mining." Discovery information (Knowledge Discovery) in the database is a process to identify patterns / templates of valid data, innovative, useful and, in the last measure, understandable.data mining, knowledge discovery, data warehouse, data mining tools, data mining applications

TEXT MINING – PREREQUISITE FOR KNOWLEDGE MANAGEMENT SYSTEMS

Text mining is an interdisciplinary field with the main purpose of retrieving new knowledge from large collections of text documents. This paper presents the main techniques used for knowledge extraction through text mining and their main areas of applicability and emphasizes the importance of text mining in knowledge management systems.text mining, knowledge systems, information retrieval

Two-phased knowledge formalisation for hydrometallurgical gold ore process recommendation and validation

This paper describes an approach to externalising and formalising expert knowledge involved in the design and evaluation of hydrometallurgical process chains for gold ore treatment. The objective was to create a case-based reasoning application for recommending and validating a treatment process of gold ores. We describe a twofold approach. Formalising human expert knowledge about gold mining situations enables the retrieval of similar mining contexts and respective process chains, based on prospection data gathered from a potential gold mining site. Secondly, empirical knowledge on hydrometallurgical treatments is formalised. This enabled us to evaluate and, where needed, redesign the process chain that was recommended by the first aspect of our approach. The main problems with formalisation of knowledge in the domain of gold ore refinement are the diversity and the amount of parameters used in literature and by experts to describe a mining context. We demonstrate how similarity knowledge was used to formalise literature knowledge. The evaluation of data gathered from experiments with an initial prototype workflow recommender, Auric Adviser, provides promising results

Knowledge transfer inside the regional economic system: the case of eighty years of economic history of the Russian North-East

Economic role of the knowledge transfer is studied on the example of the Russian North-East and its two basic branches that is exploration and mining which form the core of the regional economy (regional mining system). Russian North-East can be considered as isolated industrial district with the basic gold mining activity. The scale of this activity has been determined regional socio-economic development for the last 80 years. The major information for this study has been taken from GIS on the spatial structure of exploration and mining branches in the Russian North-East on the district level for the last 80 years. On the basis of revealed regularities and specifics in the interregional knowledge transfer we have formed general understanding of this process. We have analyzed major channels of knowledge transfer from exploration as the branch generating new knowledge on the mineral resources to mining in which this knowledge is utilized. We have determined major systems of knowledge transfer from one branch to the other under different periods of regional economic history. We have examined how territorial structures of exploration and mining industries had been changed inside the regional system for the last 80 years. Main characteristics of knowledge transfer inside the regional mining system influence its productivity (volume of extraction, speed of development of the new deposits, etc.). Effective communication between exploration and mining industry, knowledge transfer from the geologists to the miners is critically important for the sustainable work of the regional mining system.

The Coron System

Coron is a domain and platform independent, multi-purposed data mining toolkit, which incorporates not only a rich collection of data mining algorithms, but also allows a number of auxiliary operations. To the best of our knowledge, a data mining toolkit designed specifically for itemset extraction and association rule generation like Coron does not exist elsewhere. Coron also provides support for preparing and filtering data, and for interpreting the extracted units of knowledge