Knowledge Discovery from Databases

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An Online Classification and Prediction Hybrid System for Knowledge Discovery in Databases

An Online Classification and Prediction Hybrid System for Knowledge Discovery in Databases

This paper describes an augmented hybrid system, called Rated MCRDR (Multiple Classification Ripple Down Rules [4]) or simply RM. This method provides a means for a domain expert to incrementally build a knowledge base that can be used for classification and prediction in a large database, without the need for pre-sorting or the retention of large training sets in resident memory. Additionally, the expert only needs to review the occasional special case, providing a semi-automated process with the advantage of built in expert knowledge. The system takes MCRDR conclusions and their justifications and feeds them into a purpose built RBF Neural Network and trained using the single-step-∆- update-rule [5]. RM is capable of classifying database tuples into single or multiple classifications. Additionally, RM has been shown to be able to provide a prediction or evaluation for continuous value ranges [5]. MCRDR has previously been shown to be a highly effective incremental learning Knowledge Based System (KBS)[4, 6, 7]. It allows a domain expert to add rules online by providing justifications identifying the differences between cases within the context provided. The methodology has also been shown to provide significantly more compact rule bases than decision tree based systems such as C4.5 [4, 8]. Additionally, due to its lack of requiring a training-set it does not suffer the memory problems inherit in decision tree systems. Thus, with the exception of one major drawback, MCRDR could provide a highly effective system for knowledge discovery in large databases. The inherit problem with using MCRDR for KDD is that the human expert must be in a position to review each and every case to ensure that it is classified correctly. Therefore, this does not scale to large databases and clearly prevents its application as a data-mining tool.

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Knowledge discovery in databases of biomechanical variables: application to the sit to stand motor task

Knowledge discovery in databases of biomechanical variables: application to the sit to stand motor task

edge" refers here to any relationship among attributes associated with the phenomenon under analysis. These relationships can be intended as causal and, therefore, suitable for the interpretation endeavours, or at least as tools for evidencing the presence of a repeatable pattern of variables. The declared goal was pursued by searching relationships among large amounts of biomechanical quantities by using an automatic method. Some data min- ing techniques (data mining is a step of a process called Knowledge Discovery in Databases (KDD)) lend them- selves to be effectively used in this context since they may reveal meaningful patterns and data structures from mas- sive databases [10,11]. A specific data mining technique was applied to the data yielded by the analysis of sit-to- stand (STS) trials performed by healthy adults and carried out using the above-mentioned MMIM approach. The STS motor task was chosen because it has been shown to be adequate for determining the level of subject-specific motor ability [12]. In addition, the data provided by MMIMs were shown to be powerful overall descriptors of motor tasks. A group of unrestricted age and gender healthy adults was used with the goal of discovering knowledge inherent to the way healthy adults perform the selected motor task.

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Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases

Finding Most Effective Sequences of Modules for Students in Higher Education to Improve their Performance using Knowledge Discovery in Databases

Mainly, the challenge in the presented study is to predict the student performance based on the collection of attributes providing information about the sequence of modules taken by the student at each level and each track. The concept to be learned by DM algorithm is the “GPA”, positive or negative thus it has been selected as the target attribute in this case. First, an attempt to create a classifier with a common dataset for all levels and track was made but the result was mediocre with very low accuracy and understandable DT. Hence, for precision and accuracy concerns, we search for sequences of courses in each semester and track separately. Thus, data was divided into 3 groups corresponding to common levels: 3, 4, 5 and 9 groups corresponding to levels 6, 7 and 8 including tracks NSN, WTM, DAM for each semester. 12 excel files were saved, as Comma Delimited (.csv) files and then transformed to an Attribute Relation File Format (.arff) file which is better when extracting the knowledge using DM techniques. The final dataset used for the current study all levels and track combined contains 4800 instances.

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Self-Organizing Maps For Knowledge Discovery From Corporate Databases To Develop Risk Based Prioritization For Stagnation 

Self-Organizing Maps For Knowledge Discovery From Corporate Databases To Develop Risk Based Prioritization For Stagnation 

Company databases in an unprocessed format do not easily lend themselves to direct analysis to establish any relationships or trends in the data. The databases are generally on separate platforms, in differing formats, with non-uniform IDs and contain many unpopulated or company specific default value data fields in their raw form. Previous work has examined asset databases and customer service records providing information relating to bursts leakage and water quality complaints for example Unwin et al. [4]. This was primarily based on proximity searches and visual mining.

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Knowledge discovery in databases: application to chromatography

Knowledge discovery in databases: application to chromatography

The classication of learning strategies shown in Table 1 allows Machine Learning techniques to be compared in terms of the types of external information that they use and their strategies and methods. The inference capabilities of machine learning systems vary. No inference is needed in rote learning as the environment provides information exactly at the level needed to perform the task. In learning from instruction the information provided by the environment is general or abstract and the learning system must perform some inference to ll in the details. The deductive and inductive learning strategies must be capable of performing their particular modes of inference but they place a smaller burden on the external environment than the strategies mentioned above. Analogical strategies require both inductive and deductive capabilities: nding common substructure involves induction whereas performing analogical mapping is a form of deduction.

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Knowledge discovery for large databases in education institutes

Knowledge discovery for large databases in education institutes

Classification is a data mining technique that predicts data elem ents’ group, for example we can predict the weather o f a day will be sunny, rainy or cloudy. In classification we have predefined classes that classification is a task to assign instances to these classes opposite o f clustering that we don’t have knowledge about group definitions. In clustering we cluster elements based on their attribute on the contrary in classifying we classify elements into groups by recognizing pattern.

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Title: Enhancing Clustering Mechanism by Customised Expectation– Maximization Algorithm: A Review

Title: Enhancing Clustering Mechanism by Customised Expectation– Maximization Algorithm: A Review

Relational database management systems & desktop statistics packages often have difficulty handling big data. work instead requires parallel software running on tens, even thousands of servers". What is considered "big data" varies depending on capabilities of users & their tools, & expanding capabilities make big data a moving target. "For some business facing hundreds of gigabytes of data for first time might trigger a need to reconsider data management options. For others, this might take tens or hundreds of TB before data size becomes a important consideration. Data mining (the analysis step of "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is computational process of discovering system in large data sets involving methods at intersection of artificial intelligence, statistics, & database systems. overall goal of data mining is process of extract information from a data set & transform this into an understandable structure for further use. Aside from raw analysis step, this involves database & data management aspects, data pre-processing, model & inference considerations, interestingness metrics, complexity, post-processing of discovered structures, visualization, & online updating. term is a misnomer, because goal is extraction of patterns & knowledge from large amount of data, not extraction of data itself. It also is a buzzword & is frequently applied to any form of big range data or information processing (collection, extraction, analysis, & statistics) as well as computer decision support system,

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Performance of Different Statistical Techniques on Indian Administrative Data by Using GeoDa

Performance of Different Statistical Techniques on Indian Administrative Data by Using GeoDa

Spatial databases reside terabytes of spatial data that may be obtained from topographic maps, aerial photos, satellite images, medical equipment’s, laser scanners, video cameras among others in public and private organizations which also access several databases comprising census, economic, security, and statistical information for enterprise business processes[2]. It is costly and often unrealistic for users to examine spatial data in detail and search for meaningful patterns or relationships among data. Spatial data mining (SDM) aims to automate such a knowledge discovery process in large databases along with visual exploration techniques for correct communication.

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DATA MINING MADE SIMPLE BY APRIORI ALGORITHM FOR MARKET ANALYSIS

DATA MINING MADE SIMPLE BY APRIORI ALGORITHM FOR MARKET ANALYSIS

Knowledge discovery is an emerging field which combines the techniques from mathematics, statistics, algorithms and Artificial Intelligence to extract the knowledge. Data mining is a main phase of Knowledge Discovery in Databases (KDD) for extracting the knowledge based on the patterns and their correlation by the application of appropriate association rules to the information’s available from the data set. The outcome of the KDD is used to analyze or predict on the future aspects in any area of considerations. In this paper we propose an analysis and prediction of the market sales based on the historical information’s from the database by considering the items information at different levels to generate the association rules. The widely used algorithm in data mining ie, apriori algorithm is specifically considered for the extraction of the knowledge.

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Spatial Data Mining, latitude and longitude

Spatial Data Mining, latitude and longitude

Abstract— In this paper we are finding the correction location of user based on latitude and longitude is big challenge in spatial database. Clustering is an efficient technique to group together data set to obtain accurate maps. We discuss the k- means clustering algorithm in detail with Spatial Data Mining and how this method can be used. To address these challenges, spatial data mining and geographic knowledge discovery has emerged as an active research field, focusing on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. Results confirm that k-means clustering can be used to obtainmost visited region of user throughout getting the latitude and longitude and search in google map database.

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To Study the Various Methods used in Data Mining

To Study the Various Methods used in Data Mining

The development of Information Technology has generated large amount of databases and huge data in various areas. The research in databases and information technology has given rise to an approach to store and manipulate this precious data for further decision making. Data mining is a process of extraction of useful information and patterns from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data /pattern analysis. To generate information it requires massive collection of data. The data can be simple numerical figures and text documents, to more complex information such as spatial data, multimedia data, and hypertext documents. To take complete advantage of data; the data retrieval is simply not enough, it requires a tool for automatic summarization of data, extraction of the essence of information stored, and the discovery of patterns in raw data. With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important, to develop powerful tool for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. The only answer to all above is ‗Data Mining‘. Data mining is the extraction of hidden predictive information from large databases; it is a powerful technology with great potential to help organizations focus on the most important information in their data warehouses (Fayyad 1996). Data mining tools predict future trends and behaviors, helps organizations to make proactive knowledge-driven decisions (Fayyad 1996). The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer the questions that traditionally were too time consuming to resolve. They prepare databases for finding hidden patterns, finding predictive information that experts may miss because it

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To Study the Various Techniques or its Applications in Data Mining

To Study the Various Techniques or its Applications in Data Mining

To best apply these advanced techniques, they must be fully integrated with a data warehouse as well as flexible interactive business analysis tools. Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and analyzing the data. Furthermore, when new insights require operational implementation, integration with the warehouse simplifies the application of results from data mining. The resulting analytic data warehouse can be applied to improve business processes throughout the organization, in areas such as promotional campaign management, fraud detection, new product rollout, and so on. Figure 1.3 illustrates architecture for advanced analysis in a large data warehouse.

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Title: A Study on Different Classification Models for Knowledge Discovery

Title: A Study on Different Classification Models for Knowledge Discovery

In Year 2006, Carlos Ordonez performed a work," Association Rule Discovery With the Train and Test Approach for Heart Disease Prediction". Association rules represent a promising technique to improve heart disease prediction. Author introduce an algorithm that uses search constraints to reduce the number of rules, searches for association rules on a training set, and finally validates them on an independent test set. The medical significance of discovered rules is evaluated with support, confidence, and lift. Association rules are applied on a real data set containing medical records of patients with heart disease. In medical terms, association rules relate heart perfusion measurements and risk factors to the degree of disease in four specific arteries.

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EDM: An analysis of learner’s academics performance based on  Frequent Pattern Tree Algorithm

EDM: An analysis of learner’s academics performance based on Frequent Pattern Tree Algorithm

Abstract - The education system performance of school education in India is a turning point in the academic lives of all learners. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for learners to determine factors that influence the learner’s performance. Educational data mining is used to analyse the data available in the educational field and elicit the hidden knowledge from it. In this study, a survey cum experimental methodology is implemented to generate a database and it was constructed from school education department. The raw data is pre-processed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 10,000 student examination records, which is use in implementation stage. This paper implement the generalized sequential pattern mining algorithm for finding frequent patterns from learner’s database and frequent pattern tree algorithm to build the tree based on frequent patterns. This tree can be used for predicting the learner’s performance as pass or fail.

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Knowledge Discovery from Business Contracts.

Knowledge Discovery from Business Contracts.

Many methods have been developed for IE including those based on patterns, statistics, and machine learning. Pattern-based methods use handcrafted surface patterns to extract in- formation of interest, often with strong results from closed domains [45]. Pattern-based methods are simple and effective but fall short in portability across domains. The patterns developed in one domain often do not apply in another, and the process of crafting patterns sometimes involves expert domain knowledge. Statistical methods often demonstrate strong robustness in open domain IE tasks such as open web information extraction [100]. Among these, point-wise mutual information (PMI), a statistical method, is widely used in revealing the similarity re- lations of concepts and extracting parallel concepts, e.g., using result counts obtained from a search engine. The disadvantage of this method is the requirement of a large amount of data for the statistical model to be effective. Machine learning methods produce promising results on classification and labeling tasks [34]. Classification, a widely used technique, employs a set of features to predict the class label of a new instance based on human annotated data. Different types of features can come into play: grammatical features such as the part of speech of a token; statistical features such as the term frequency (TF) of a token; and, contextual features such as the neighbors of the target token. Several classifications have been applied in various IE tasks, including some hybrid approaches [30].

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Plant Discovery from Leaf

Plant Discovery from Leaf

Abstract:- The human visual system has no problem in interpreting the subtle variations in colour and shading in a photograph and correctly recognize the object from its background. Suppose if a person takes a field trip, and when he or she sees a plant on the ground, that person would like to gain information about the plant. With the help of a mobile camera and a recognition program, we enable our users to get useful information of the plant’s leaf which they have taken. The sole purpose of this project is to develop a leaf recognition algorithm based on its specific characteristics. For this photograph of the leaf is taken in a mobile camera and send it to the image processing an application. After processing the image result is send back to the mobile. Result contains the name, species, life span, industrial and medicinal usages of the desired image. This is done by preparing dataset of the plants beforehand. Proposed algorithm is performed as three stages viz preprocessing, extraction of features, classification. In Preprocessing one usually process the image data so it should be in suitable form which means one could single out each objects after this step. In second step measure the features of intended objects. Class of object based on features are determined in the final step.

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Knowledge Discovery from Dynamic Data on a Nonlinear System

Knowledge Discovery from Dynamic Data on a Nonlinear System

This study has proposed a knowledge discovery system for determining the operational state of a nonlinear sys- tem based on an inspection of its dynamic data. In the proposed approach, a synchronized phasor measurement technique is used to acquire the dynamic data of the nonlinear system, and a hyper-rectangle type neural network (HRTNN) is then applied to extract a set of fuzzy rules for determining the system stability. The effectiveness of the proposed approach has been demonstrated using a real-world AEP-14 bus system for illustration purposes. Moreover, the validity of the extracted rules has been confirmed by the means of a two-stage CFA investigation. Finally, the overall performance of the proposed system has been demonstrated by evaluating six Fitness Indexes. The results have shown that the proposed methodology represents a feasible basis for the development of an ef- fective rule-based system for knowledge discovery on real-life dynamic nonlinear systems.

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Unsupervised Discovery of Domain Specific Knowledge from Text

Unsupervised Discovery of Domain Specific Knowledge from Text

To derive the classes used for entities, we do not re- strict ourselves to a fixed set, but derive a domain- specific set directly from the data. This step is per- formed simultaneously with the corpus generation described above. We utilize three syntactic construc- tions to identify classes, namely nominal modifiers, copula verbs, and appositions, see below. This is similar in nature to Hearst’s lexico-syntactic patterns (Hearst, 1992) and other approaches that derive IS- A relations from text. While we find it straightfor- ward to collect classes for entities in this way, we did not find similar patterns for verbs. Given a suit- able mechanism, however, these could be incorpo- rated into our framework as well.

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Association Rules Mining for Business Intelligence

Association Rules Mining for Business Intelligence

Since it is difficult to predict what exactly could be discovered from database, a high level data mining query should be treated as a probe which may disclose some interesting traces for further exploration. Interactive discovery should be encouraged, which allows a user to interactively refine a data request, dynamically change data focusing, progressively deepen a data mining process flexibly view the data mining results at multiple abstraction levels and from different stages.

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WEB BLOG MINING SURVEY

WEB BLOG MINING SURVEY

Web Blogs—commonly described as blogs—are “frequently modified Web pages in which dated entries are listed in reverse chronological sequence”. Bloggers—the people who write them—use this venue to freely express their opinions and emotions, making blogs increasingly popular. Analyzing these personal entries could even provide opportunities for governments and companies to understand the public in a way that was previously costly or even unavailable. Although the blogosphere contains a lot of useful information, the data is noisy because blog entries are unstructured and might cover a wide variety of topics. By analyzing the freely expressed opinions of bloggers via blog mining, marketers, for example, can get closer to customers and learn more about their opinions on certain products, companies, or political issues. However, because so many blogs exist, manually monitoring and analyzing them is a labor- intensive and time-consuming task. In addition, we can apply knowledge discovery algorithms to determine why such topics are popular and categorize them according

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