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The aim of this article is to describe the design, implementation and evaluation of the educational application to support learning of data mining algorithms. The role of the application is to help students to better understand the algorithms such as Naive Bayes classifier, decision trees and association rules. The application also includes a test area that allows students to generate and solve different...
Classification is the process of finding a model or function that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The goal of classification is to accurately predict the target class for each case in the data. In sequence database having sequences, in which each sequence is a list of...
Web usage mining, is the method of mining for user browsing and access patterns. Usage data captures the identity or origin of Web users along with their surfing behavior at a Web site. This paper aims to classify user behavior in identifying the patterns of the browsing and navigation data of web users and also measure the performance of the Frequent Pattern (FP) Growth algorithm and Apriori algorithm...
With the rapid development of clustering analysis technology, there have been many application-specific clustering algorithms, such as text clustering. K-Means algorithm, as one of the classic algorithms of clustering algorithms, and a textual document clustering algorithms commonly used in the analysis process, is widely used because of its simple and low complexity. This article in view of two big...
The usefulness and relevance of association rules extracted by the generation algorithms are a critical problem. In fact, in most cases, the real datasets lead to a very large number of association rules, which does not allow users to make their own selection of the most relevant. The searching of the best from the vast array of extracted rules require the identification and use of good measures or...
Today, securing data of gargantuan size is challenging especially for unstructured data item belonging to industries or business sectors. This paper reviews approaches for investigation of mining algorithms in cryptography and sheds light on application of mining algorithms in symmetric key cryptography. It makes an attempt to find out how classification and association rules can be used to distinguish...
Associative Classification is a recent and rewarding approach which combines associative rule mining and classification. This technique has attracted many researchers as it derives accurate classifier with effective rules. Associative classifiers are useful for application where maximum predictive accuracy is desired. Increasing access to huge datasets and corresponding demands to analyze these data...
Mining association rules is a fundamental data mining task. Association rule greatly help to identify trends and pattern from huge data set. Algorithms for mining association rules put more stress on positive rules rather than negative rules. Negative rules specify the attribute present in the data set to the attribute absent. In this paper we propose an algorithm BMPNAR, Best M Positive Negative...
Mining association rules is a leading task, which attracted the attention of researchers, it is one of the technical potential of data mining that allows discovered correlations and association between voluminous datasets. It generally spend two important steps, in the first is the extraction of frequent items, and extracting association rules from this frequent items for the second step. This extraction...
There are many algorithms and systems for mining data that are being constantly developed and improved by research communities and industrial organizations worldwide, but choosing the most adequate and with the most optimal results for the problem at hand remains a main concern and a critical decision to make. This paper inspects the most used algorithms and systems for data mining in order to point...
Soft-sensors have been widely used for estimating product quality or other key variables. To achieve high estimation performance for soft-sensor design, it is important to select appropriate input or explanatory variables. This paper presents a new feature selection method applied to Soft-sensors. The proposed method, referred to as FCA-ARM (fuzzy clustering analysis-association rule mining). The...
The support is generally higher when the classical apriori algorithm is used as mining data based on association rules, if the support is small low then redundant frequent item set and redundant rules are produced large, so the local effective association rules has a larger confidence and a smaller support can not be mined out, which is the fatal defects of the classical apriori algorithm. According...
With the coming of the Big Data era, data mining has been confronted with new opportunities and challenges. Some limitations are exposed when traditional association rule mining algorithms are used to deal with large-scale data. In the Apriori algorithm, scanning the external storage repeatedly leads to high I/O load and brings about low performance. As for FP-Growth algorithm, the effectiveness is...
For any successful launch of product, it should be properly reviewed. This is done by conducting a meeting in which various participants give their opinions, and depending upon those opinions decision is made. Firstly, it was given by Anvil tool, which was difficult to analyze. Also, commands such as propose, acknowledgement, negative response do not have a predefined notion which can differentiate...
In this paper we proposes an efficient approach based on apriori algorithm. We use density minimum support so that we reduce the execution time. Our approach supports the zonal minimum support, by this approach we can store the transaction on the daily basis, then we provide three different density zone based on the transaction and minimum support which is low(L), Medium(M), High(H). Based on the...
Knowledge extraction is a process of filtering some informative knowledge from the database so that it can be used wide variety of applications and analysis. Due to this highly efficient algorithm is required for data mining and for accessing data from large datasets. Although there are various techniques implemented for the detection of anomalies using frequent item sets using apriori algorithm but...
Web data mining is a key tool for e-commerce in such an age of Internet. Due to previous studies, there is no such a mining technology superior to others. Therefore, this paper can give a simplified comprehension of web data mining and indicate the improvement direction of each kind of mining algorithm based on their existing defects. This paper first introduces the main process of data mining, including...
The main difference of the associative classification algorithms is how to mine frequent item sets, analyze the rules exported and use for classification. This paper presents an associative classification algorithm based on Trie-tree that named CARPT, which remove the frequent items that cannot generate frequent rules directly by adding the count of class labels. And we compress the storage of database...
This paper analyzes the advantages and disadvantages of equipment, combat simulation data association rules commonly used in the analysis of discrete algorithms, and it proposed and implemented a discretization algorithm based on attribute importance and incompatible degrees, through theoretical research and analysis of algorithm, and experimental comparison, it proves the correctness and validity...
Association rule is an important model in data mining. However, traditional association rules are mostly based on the support and confidence metrics, and most algorithms and researches assumed that each attribute in the database is equal. In fact, because the user preference to the item is different, the mining rules using the existing algorithms are not always appropriate to users. By introducing...
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