Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it. We review several existing frameworks for DM research that originate from different paradigms. These DM frameworks mainly address various DM algorithms for the different steps of the DM process. Recent research has shown that many real-world problems require integration of...

We describe the Data Mining OPtimization Ontology (DMOP), which was developed to support informed decision-making at various choice points of the knowledge discovery (KD) process. It can be used as a reference by data miners, but its primary purpose is to automate algorithm and model selection through semantic meta-mining, i.e., ontology-based meta-analysis of complete data mining processes in ...

Data mining (DM) techniques have gained a lot of attention lately as they may provide decision-makers with institutional knowledge required for making strategic decisions. Such techniques are able to handle and extract useful information from large data sets. Many commercial tools are now available for the novices to facilitate the data mining process. However, treating these tools as a black b...

Knowledge Discovery in Databases (KDD) is the process of automatic discovery of previously unknown patterns, rules, and other regular contents implicitly present in large volumes of data. Data Mining (DM) denotes discovery of patterns in a data set previously prepared in a specific way. DM is often used as a synonym for KDD. However, strictly speaking DM is just a central phase of the entire pr...

Abstrak - UPT Perpustakaan Universitas Lampung merupakan yang bergerak di bidang perpustakaan. Memiliki dua layanan berdasarkan interaksinya yaitu teknis dan pengguna. Saat ini memiliki buku tercetak sebanyak 142.776. Penelitian bertujuan menemukan pola association rule dengan teknik data mining memanfaatkan software RapidMiner 9.1 dalam penerapan algoritma Apriori. Metode penelitian Cross Indu...

This research paper is about constructive induction of data mining (DM) which is used to cope with inadequacy of attributes. Constructive induction is a process of learning concept description that represents best hypothesis in the space. Inductive learning algorithms are increasingly being pressed into service, as data mining and knowledge discovery tools, to detect patterns or regularities in...

Business Process Redesign (BPR) is undertaken to achieve order-of-magnitude improvements over ‘old’ form of the organisation. Practitioners in the academia and business world have developed a number of methodologies to support this competitive restructuring that forms the current focus of concern, many of which have not been successful. This paper suggests the use of Data Mining (DM) as a techn...

fuzzy rule-based classification system (frbcs) is a popular machine learning technique for classification purposes. one of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. however many cases the minority classes are more important than the majority ones. in this paper, we have extended ...

Data mining (DM) has emerged as one of the key features of many applications on information system. While Data Analysis (DA) represents a significant advance in the type of analytical tools currently available, there are limitations to its capability. In order to address one of the limitations on the DA capabilities of identifying a causal relationship, we propose an integrated approach, called...

It is clear that the failures found in hospital management are usually related to the lack of information and insufficient resources management. The use of Data Mining (DM) can contribute to overcome these limitations in order to identify relevant data on patient’s management and providing important information for managers to support their decisions. Throughout this study, were induced DM mode...