30 research outputs found
Mining Target-Oriented Sequential Patterns with Time-Intervals
A target-oriented sequential pattern is a sequential pattern with a concerned
itemset in the end of pattern. A time-interval sequential pattern is a
sequential pattern with time-intervals between every pair of successive
itemsets. In this paper we present an algorithm to discover target-oriented
sequential pattern with time-intervals. To this end, the original sequences are
reversed so that the last itemsets can be arranged in front of the sequences.
The contrasts between reversed sequences and the concerned itemset are then
used to exclude the irrelevant sequences. Clustering analysis is used with
typical sequential pattern mining algorithm to extract the sequential patterns
with time-intervals between successive itemsets. Finally, the discovered
time-interval sequential patterns are reversed again to the original order for
searching the target patterns.Comment: 11 pages, 9 table
Efficient Incremental Breadth-Depth XML Event Mining
Many applications log a large amount of events continuously. Extracting
interesting knowledge from logged events is an emerging active research area in
data mining. In this context, we propose an approach for mining frequent events
and association rules from logged events in XML format. This approach is
composed of two-main phases: I) constructing a novel tree structure called
Frequency XML-based Tree (FXT), which contains the frequency of events to be
mined; II) querying the constructed FXT using XQuery to discover frequent
itemsets and association rules. The FXT is constructed with a single-pass over
logged data. We implement the proposed algorithm and study various performance
issues. The performance study shows that the algorithm is efficient, for both
constructing the FXT and discovering association rules
Frequent Item Set Mining Using INC_MINE in Massive Online Analysis Frame Work
Frequent Pattern Mining is one of the major data mining techniques, which is exhaustively studied in the past decade. The technological advancements have resulted in huge data generation, having increased rate of data distribution. The generated data is called as a ‘data stream’. Data streams can be mined only by using sophisticated techniques. The paper aims at carrying out frequent pattern mining on data streams. Stream mining has great challenges due to high memory usage and computational costs. Massive online analysis frame work is a software environment used to perform frequent pattern mining using INC_MINE algorithm. The algorithm uses the method of closed frequent mining. The data sets used in the analysis are Electricity data set and Airline data set. The authors also generated their own data set, OUR-GENERATOR for the purpose of analysis and the results are found interesting. In the experiments five samples of instance sizes (10000, 15000, 25000, 35000, 50000) are used with varying minimum support and window sizes for determining frequent closed itemsets and semi frequent closed itemsets respectively. The present work establishes that association rule mining could be performed even in the case of data stream mining by INC_MINE algorithm by generating closed frequent itemsets which is first of its kind in the literature
Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised)
Most current clustering based anomaly detection methods use scoring schema
and thresholds to classify anomalies. These methods are often tailored to
target specific data sets with "known" number of clusters. The paper provides a
streaming clustering and anomaly detection algorithm that does not require
strict arbitrary thresholds on the anomaly scores or knowledge of the number of
clusters while performing probabilistic anomaly detection and clustering
simultaneously. This ensures that the cluster formation is not impacted by the
presence of anomalous data, thereby leading to more reliable definition of
"normal vs abnormal" behavior. The motivations behind developing the INCAD
model and the path that leads to the streaming model is discussed.Comment: 13 pages; fixes typos in equations 5,6,9,10 on inference using Gibbs
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