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

Knowledge data discovery and data mining in a design environment

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development

Knowledge Discovery in Data Mining and Massive Data Mining

Knowledge discovery is a process of non trivial extraction of previously unknown and presently useful information. The rapid advancement of the technology resulted in the increasing rate of data distributions. The data generated from mobile applications, sensor applications, network monitoring, traffic management, weblogs etc. can be referred as a data stream. The data streams are massive in nature. The present work mainly aims at knowledge discovery using data mining and massive data mining techniques. The knowledge discovery process in both the techniques is compared by developing a classification model using Naive bayes classifier. The former case uses Edu-data, a data collected from technical education system and the latter case uses massive online analysis frame work to generate the data streams. Mining data stream is referred as Massive Data Mining. The data streams must be processed under very strict constraints of space and time using sophisticated techniques. The traditional data mining techniques are not advised on this massive data. Therefore the massive online analysis framework is used to mine the data streams. The present work happens to be unique in the literaturein

Knowledge discovery: data and text mining

Data mining and text mining refer to techniques, models, algorithms, and processes for knowledge discovery and extraction. Basic de nitions are given together with the description of a standard data mining process. Common models and algorithms are presented. Attention is given to text clustering, how to convert unstructured text to structured data (vectors), and how to compute their importance and position within clusters

ADVANCES IN KNOWLEDGE DISCOVERY IN DATABASES

The Knowledge Discovery in Databases and Data Mining field proposes the development of methods and techniques for assigning useful meanings for data stored in databases. It gathers researches from many study fields like machine learning, pattern recognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, data visualization and grids. While Data Mining represents a set of specific algorithms of finding useful meanings in stored data, Knowledge Discovery in Databases represents the overall process of finding knowledge and includes the Data Mining as one step among others such as selection, pre�processing, transformation and interpretation of mined data. This paper aims to point the most important steps that were made in the Knowledge Discovery in Databases field of study and to show how the overall process of discovering can be improved in the future.

Geographic Data Mining and Knowledge Discovery

Geographic data are information associated with a location on the surface of the Earth. They comprise spatial attributes (latitude, longitude, and altitude) and non-spatial attributes (facts related to a location). Traditionally, Physical Geography datasets were considered to be more valuable, thus attracted most research interest. But with the advancements in remote sensing technologies and widespread use of GPS enabled cellphones and IoT (Internet of Things) devices, recent years witnessed explosive growth in the amount of available Human Geography datasets. However, methods and tools that are capable of analyzing and modeling these datasets are very limited. This is because Human Geography data are inherently difficult to model due to its characteristics (non-stationarity, uneven distribution, etc.). Many algorithms were invented to solve these challenges -- especially non-stationarity -- in the past few years, like Geographically Weighted Regression, Multiscale GWR, Geographical Random Forest, etc. They were proven to be much more efficient than the general machine learning algorithms that are not specifically designed to deal with non-stationarity. However, such algorithms are far from perfect and have a lot of room for improvement. This dissertation proposed multiple algorithms for modeling non-stationary geographic data. The main contributions are: (1) designed a novel method to evaluate non-stationarity and its impact on regression models; (2) proposed the Geographic R-Partition tree for modeling non-stationary data; (3) proposed the IDW-RF algorithm, which uses the advantages of Random Forests to deal with extremely unevenly distributed geographic datasets; (4) proposed the LVRF algorithm, which models geographic data using a latent variable based method. Experiments show that these algorithms are very efficient and outperform other state-of-the-art algorithms in certain scenarios

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Applications of concurrent access patterns in web usage mining

This paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns (CAP), where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access pat-terns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with real-world datasets highlight the applicability of the approach in web navigation

Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

Knowledge Discovery using Various Multimedia Data Mining Technique

Knowledge discovery in databases (KDD) is the process of discovering positive information from a gathering of data. This generally used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior information on data sets and interpreting perfect solutions from the observed results. Knowledge Discovery in Databases is the process of finding knowledge in huge amount of data where data mining is the core of this process. Data mining can be used to understandable meaningful patterns from huge databases and these patterns may be transformed into knowledge. Multimedia data mining can be defined as the process of finding motivating patterns from media data such as audio mining , video mining, image mining and text mining that are not generally available by basic queries and associated results. It is the mining of knowledge and high level multimedia information from large multimedia database system. Multimedia data mining refers to sample discovery, rule extraction and knowledge gaining from multimedia database. In this paper, An Overview On various Multimedia Data technique is given and the main focus is given to the video Data Mining. DOI: 10.17762/ijritcc2321-8169.15035