Konstantinos F. Xylogiannopoulos

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This research paper focuses on data mining in time series and its applications on financial data. Data-mining attempts to analyze time series and extract valuable information about pattern periodicity, which might be concealed by substantial amounts of unformatted, random information. Such information, however, is of great importance as it can be used to(More)
One of the issues with using social networks for analysis is the problem of having missing nodes in the network. Having missing nodes can significantly impact the results of the analysis, and should be avoided as much as possible. For social network analysis to be more effective for criminal networks, where there are likely missing nodes, the number of(More)
Suffix arrays form a powerful data structure for pattern detection and matching. In a previous work, we presented a novel algorithm (COV) which is the only algorithm that allows the detection of all repeated patterns in a time series by using the actual suffix array. However, the requirements for storing the actual suffix strings even on external media(More)
Suffix array is a powerful data structure, used mainly for pattern detection in strings. The main disadvantage of a full suffix array is its quadratic O(n 2 ) space capacity when the actual suffixes are needed. In our previous work [39], we introduced the innovative All Repeated Patterns Detection (ARPaD) algorithm and the Moving Longest Expected Repeated(More)
In everyday life bulk amount of time-stamped data is accumulated in diverse databases. Such data may be mapped into a time-based representation forming very long time series which could be effectively analyzed for valuable knowledge discovery. However, most of the times analyzing these time series has been proven a very complicated task especially when they(More)
In the medical practice in countries like Canada, it is common that a general practitioner (GP) refers a patient to a specialist (SP) to complete the patients' medical treatment. This patients' transfer is termed medical referral process. Recently, many researchers have been focussing on reducing the wait time spent on the medical referrals which is mainly(More)
Sequential frequent itemsets detection is one of the core problems in data mining. In the current paper we propose a new methodology based on our previous work regarding the detection of all repeated patterns in a string. By analyzing big datasets from FIMI website of up to one million transactions we were able to detect not only the most frequent(More)
Frequent pattern mining and consequently association rule mining is a useful technique for discovering relationships between items in databases. However, as the size of the data to be analyzed increases or the values of the pruning thresholds decrease, larger number of frequent pattern and more association rules will be generated with little information(More)