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This paper presents AgriMine which is a tool that was developed in order to mine agricultural problems accumulated in a textual database over a period of 5 years. The problems, which are accompanied by their solutions, offer a wealth of knowledge that can be used by decision makers, researchers, and farmers alike. However, this wealth of knowledge cannot be unlocked without first representing these...
When digging association rules among items, the items are dealt in an equal way. However, it is usually not happen in databases in the real world. Different items always have different importance. To reflect them, The way of draw weight into items and use weight association rules can solve the problem. But weighted association rules arithmetic of these research based on Apriori arithmetic at the present,...
The development of database technology has solved the memory and retrieval of substantive data, but the biomedicine database existing the phenomenon of “data rich, information poor”. In order to solve the problem of Knowledge Discovery in Database, great importance has been continuously attached to the data mining. In this paper, we elaborate the Particularities and Key issues of data mining in biomedicine,...
Group-by is a core database operation that is used extensively in data analysis and decision support systems. In many application scenarios, it appears useful to group values according to their compliance with a certain concept instead of founding the grouping on value equality. In this paper, we propose a new SQLf construct that supports fuzzy-partition-based group-by (FGB). We show that FGB can...
Combining with the data mining application in database intrusion detection, for the most representative association rule Apriori algorithm in the data mining technology, this paper presents an improved association rule algorithm, based on which constructs a database intrusion detection system on the basis of association rules, and carries out a small range test. Experimental results show that the...
The main goal to extract knowledge in database is to help the user to give semantics of data and to optimize the information research. Unfortunately, this fundamental constraint is not taken into account by almost all the approaches for knowledge discovery. Indeed, these approaches generate a big number of rules that are not easily assimilated by the human brain. In this paper, we propose a new approach...
Many web databases can be seen as providing partial and overlapping information about entities in the world. To answer queries effectively, we need to integrate the information about the individual entities that are fragmented over multiple sources. At first blush this is just the inverse of traditional database normalization problem - rather than go from a universal relation to normalized tables,...
A new algorithm, which is based on partial support tree (PS_Tree), is proposed to deal with the incremental updating problem when a new database is inserted and the minimum support is not changed. This algorithm use effectively the association rules mined and the partial support tree reserved to improve the performance. It only need scan the updated part of the database once so that the efficiency...
This paper analyzed the datum characteristics of clinical data and diseases database of the traditional Chinese Medicine (TCM) tongue diagnosis. A mining algorithm of association rules have been advanced,which mines the diagnosis rules. The algorithm presented a more efficiency method of quantitative description about strength of association relationship.Meanwhile the paper improved FP-growth mining...
Intrusion detection analyzes unauthorized accesses and malicious behaviors and finds intrusion behaviors and attempts by detecting the state and activity of an operation system to provide an effective means for intrusion defense. Applying the intrusion detection technology to databases is an effective method of enabling databases to have positive and active security mechanisms. This paper makes an...
In SCM, the problem with RFID data is that the volume increases according to time and location, thus, resulting in an enormous degree of data duplication. Therefore it is difficult to extract useful knowledge hidden in data using existing association rule mining techniques, or analyze data using statistical techniques or queries. However, strong associations discovered at high concept levels may represent...
With the advancement of their information technology, many enterprises have accumulated a large amount of business data. We hope to analyze these data on a higher level in order to use then better. The current database systems are unable to find the association rules in data, and cannot predict the developing trend and lack method mining information and knowledge hidden behind the data information...
One of the central problems in knowledge discovery in databases, more precisely in the field of association rule mining, relies on the very large number of rules that classic rule mining systems extract. This problem is usually solved by means of a post-processing step, that filters the entire volume of extracted rules, in order to output only a few potentially interesting ones. This article presents...
Time related association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, a generalized class association rule mining is proposed using genetic network programming (GNP) in order to find time related sequential rules more efficiently. GNP has been applied to generate the candidates of the time related association rules as a tool. For fully utilizing the potential...
Finding the large item set fast is the crucial step in the association rule algorithm. In this paper we apply granular computing and quotient space to frequent item set discovering , by partition the information system to information granule and mapping granule object sets, the algorithm reduced the number of database scanning and reduced object sets required when computing support of candidate item,...
Through the study of Apriori algorithm we discover two aspects that affect the efficiency of the algorithm. One is the frequent scanning database, the other is large scale of the candidate itemsets. Therefore, IApriori algorithm is proposed that can reduce the times of scanning database, optimize the join procedure of frequent itemsets generated in order to reduce the size of the candidate itemsets...
Efficient algorithms to discover frequent patterns are crucial in data mining research. Finding frequent item sets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we present a more efficient approach for mining complete sets of frequent item sets. It is a modification of FP-tree. The contribution...
We propose here an efficient data mining algorithm to hide collaborative recommendation association rules when the database is updated, i.e., when a new data set is added to the original database. For a given predicted item, a collaborative recommendation association rule set [10] is the smallest association rule set that makes the same recommendation as the entire association rule set by confidence...
Survey data provide rich source of knowledge. However, a survey database normally contains missing data. This paper proposes a rough set based unconventional knowledge discovery method to explore missing data. It presents an experiment on a student teaching evaluation database.
To deal with the problem of too many results returned from a Web database in response to a user query, this paper proposes a novel approach, which takes advantage of the contextual preferences to precompute a few representative orders of tuples and uses them to expeditiously provide ranked answers factoring in the information contained in the query. Contextual preferences take the form that item i...
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