Multilevel Pattern Mining Architecture for Automatic Network Monitoring in Heterogeneous Wireless Communication Networks

The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.

Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining

Holistic understanding of wind behaviour over space,time and height is essential for harvesting wind energy application.This study presents a novel approach for mapping frequent wind profile patterns using multidimensional sequential pattern mining(MDSPM).This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded(0.125°×0.125°)wind data for the Netherlands every 6 h and at six height levels.The wind data were first transformed into two spatio-temporal sequence databases(for speed and direction,respectively).Then,the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multidimensional sequential patterns,which were then visualized using a 3D wind rose,a circular histogram and a geographical map.These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines.Our analysis identified four frequent wind profile patterns.One of them highly suitable to harvest wind energy at a height of 128 m and 68.97%of the geographical area covered by this pattern already contains wind turbines.This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.

From Sequential Pattern Mining to Structured Pattern Mining： A Pattern-Growth Approach

Sequential pattern mining is an important data mining problem with broadapplications. However, it is also a challenging problem since the mining may have to generate orexamine a combinatorially explosive number of intermediate subsequences. Recent studies havedeveloped two major classes of sequential pattern mining methods: (1) a candidategeneration-and-test approach, represented by (ⅰ) GSP, a horizontal format-based sequential patternmining method, and (ⅱ) SPADE, a vertical format-based method; and (2) a pattern-growth method,represented by PrefixSpan and its further extensions, such as gSpan for mining structured patterns.In this study, we perform a systematic introduction and presentation of the pattern-growthmethodology and study its principles and extensions. We first introduce two interestingpattern-growth algorithms, FreeSpan and PrefixSpan, for efficient sequential pattern mining. Then weintroduce gSpan for mining structured patterns using the same methodology. Their relativeperformance in large databases is presented and analyzed. Several extensions of these methods arealso discussed in the paper, including mining multi-level, multi-dimensional patterns and miningconstraint-based patterns.

Finding frequent trajectories by clustering and sequential pattern mining

Data mining is a powerful emerging technology that helps to extract hidden information from a huge volume of historical data. This paper is concerned with finding the frequent trajectories of moving objects in spatio-temporal data by a novel method adopting the concepts of clustering and sequential pattern mining. The algorithms used logically split the trajectory span area into clusters and then apply the k-means algorithm over this clusters until the squared error minimizes. The new method applies the threshold to obtain active clusters and arranges them in descending order based on number of trajectories passing through. From these active clusters, inter cluster patterns are found by a sequential pattern mining technique. The process is repeated until all the active clusters are linked. The clusters thus linked in sequence are the frequent trajectories. A set of experiments conducted using real datasets shows that the proposed method is relatively five times better than the existing ones. A comparison is made with the results of other algorithms and their variation is analyzed by statistical methods. Further, tests of significance are conducted with ANOVA to find the efficient threshold value for the optimum plot of frequent trajectories. The results are analyzed and found to be superior than the existing ones. This approach may be of relevance in finding alternate paths in busy networks （ congestion control）, finding the frequent paths of migratory birds, or even to predict the next level of pattern characteristics in case of time series data with minor alterations and finding the frequent path of balls in certain games.

Analyzing Sequential Patterns in Retail Databases

Finding correlated sequential patterns in large sequence databases is one of the essential tasks in data mining since a huge number of sequential patterns are usually mined, but it is hard to find sequential patterns with the correlation. According to the requirement of real applications, the needed data analysis should be different. In previous mining approaches, after mining the sequential patterns, sequential patterns with the weak affinity are found even with a high minimum support. In this paper, a new framework is suggested for mining weighted support affinity patterns in which an objective measure, sequential ws-confidence is developed to detect correlated sequential patterns with weighted support affinity patterns. To efficiently prune the weak affinity patterns, it is proved that ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate sequential patterns with dissimilar weighted support levels. Based on the framework, a weighted support affinity pattern mining algorithm （WSMiner） is suggested. The performance study shows that WSMiner is efficient and scalable for mining weighted support affinity patterns.

An enhanced cascading failure model integrating data mining technique

An enhanced cascading failure model integrating data mining technique is proposed in this paper.In order to better simulate the process of cascading failure propagation and further analyze the relationship between failure chains,in view of a basic framework of cascading failure described in this paper,some significant improvements in emerging prevention and control measures,the subsequent failure search strategy as well as the statistical analysis for the failure chains are made elaborately.Especially,a sequential pattern mining model is employed to find out the association pertinent to the obtained failure chains.In addition,a cluster analysis model is applied to evaluate the relationship between the intermediate data and the consequence of obtained failure chain,which can provide the prediction in potential propagation path of cascading failure to reduce the risk of catastrophic events.Finally,the case studies are conducted on the IEEE 10-machine-39-bus test system as benchmark to demonstrate the validity and effectiveness of the proposed enhanced cascading failure model.Some preliminary concluding remarks and comments are drawn.

Customer Activity Sequence Classification for Debt Prevention in Social Security

From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern selection and classifier building very time-consuming. The fact is that, in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which axe the most strongly correlated to each target class. In this step, an aggressive strategy is employed to select a small set of sequential patterns. Secondly, pattern pruning and serial coverage test are done on the mined patterns. The patterns that pass the serial test are used to build the sub-classifier at the first level of the final classifier. And thirdly, the training samples that cannot be covered are fed back to the sequential pattern mining stage with updated parameters. This process continues until predefined interestingness measure thresholds are reached, or all samples axe covered. The patterns generated in each loop form the sub-classifier at each level of the final classifier. Within this framework, the searching space can be reduced dramatically while a good classification performance is achieved. The proposed algorithm is tested in a real-world business application for debt prevention in social security area. The novel sequence classification algorithm shows the effectiveness and efficiency for predicting debt occurrences based on customer activity sequence data.