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This paper presents an efficient one pass technique, k-SCOPE (Top k Strongly Correlated item Pair Extraction), which finds top-k strongly correlated item pairs from transaction database, without generating any candidate sets. The proposed technique uses a correlogram matrix based approach to compute support count of all the 1- and 2-itemset in a single scan over the database. From the correlogram...
Traditional sequential pattern mining deals with positive sequential patterns only, that is, only frequent sequential patterns with the appearance of items are discovered. However, it is often interesting in many applications to find frequent sequential patterns with the nonoccurrence of some items, which are referred to as negative sequential patterns. This paper analyzes three types of negative...
Several algorithms have been proposed to solve the problem of mining frequent inter-transaction item set. However, the low efficiency of support calculation for inter-transaction itemsets is still a challenging problem that eliminates the performance of mining algorithms. This paper provides inter-transaction association rule mining algorithm using effective technique for support calculations. The...
With rapid advance of the network and data mining techniques, the protection of the confidentiality of sensitive information in a database becomes a critical issue when releasing data to outside parties. Association analysis is a powerful and popular tool for discovering relationships hidden in large data sets. The relationships can be represented in a form of frequent itemsets or association rules...
The weighted association rules (WARs) mining are made because importance of the items is different. Negative association rules (NARs) play important roles in decision-making. But the misleading rules occur and some rules are uninteresting when discovering positive and negative weighted association rules (PNWARs) simultaneously. So another parameter is added to eliminate the uninteresting rules. A...
Spatial co-location and de-location patterns represent subsets of Boolean spatial feature types whose instances are often located in close/separate geographic proximity. Existing literatures pay more attention on mining colocation patterns based on distance threshold spatial relation. In this paper, we proposed a novel co-location and de-location patterns mining algorithm (CODEM) to discover useful...
Alarms association will play an important role in improving the service and reliability in modern telecommunication networks. As the network grows in size and complexity, the supervisors of network are finding it increasingly difficult to cope with the volume of alarm messages produced even from a single network fault. This paper introduces the related studies of alarms association and sequential...
The alarms correlation rules obtained on the bases of network management alarms play an important role on network management and network maintenance. Alarms correlation is a difficult problem in network fault management; sequential pattern mining can be utilized to extract episode rules from network system alarms. This paper introduces the related studies of alarms association and sequential pattern...
Negative association rules (NARs) catch mutually exclusive correlations among items. They play important roles in decision-making. But nowadays the techniques of NARs mining focus on mono-database. With the rapid development of information and communication technologies, multi-database mining is becoming more and more important. Knowledge conflicts within databases may occur when mining both the positive...
With the increasing development and application of information and communication technologies, multi-database mining is becoming more and more important. Association rules mining is the major topic in multi-database. According to Piatetsky-Shapiropsilas argument, an association rule is interesting only if the rule meets the minimum interestingness condition. In this paper, we extended this condition...
Association rules mining is an important data mining task, this paper emphatically analyzes realization skill and defects of existing algorithms. On the basis, a novel measure, named statistical correlation, which can indicate the correlation degree of multi-items in a rule, is put forward to cut association rules with independent or negative correlation, and its concept, calculating formulas and...
Mining generalized association rules is closely related to the taxonomy(is-a hierarchy) data which exists widely in retail, geography, biology and financial domains. If we use traditional method to mine the generalized association rules, it becomes inefficient because the itemsets will be huge along with the items and levels of taxonomy increasing, and it also wastes lots of time to calculate the...
Association rules mining plays an important part in the alarm correlation analysis in the telecommunication networks. A novel algorithm based on time window pre-processing and the weighted frequent pattern tree method was proposed in this paper. It is an efficient algorithm which can avoid scanning the database many times and producing a large number of conditional pattern trees. Experiments on a...
The real-world data may be usually polluted by uncontrolled factors or contained with noisy. Fault-tolerant frequent pattern can overcome this problem. It may express more generalized information than frequent pattern which is absolutely matched. The present research is integrated with previous research into an integrity new method, called Top-NFTDS, to discover fault-tolerant association rules over...
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