Directed Graph based Distributed Sequential Pattern Mining Using Hadoop MapReduce

Usual sequential pattern mining algorithms experiences the scalability problem when trade with very big data sets. In existing systems like PrefixSpan, UDDAG major time is needed to generate projected databases like prefix and suffix projected database from given sequential database. In DSPM (Distributed Sequential Pattern Mining) Directed Graph is introduced to generate prefix and suffix projected database which reduces the execution time for scanning large database. In UDDAG, for each unique id UDDAG is created to find next level sequential patterns. So it requires maximum storage for each UDDAG. In DSPM single directed graph is used to generate projected database and finding patterns. To improve the scanning time and scalability problem we introduce a distributed sequential pattern mining algorithm on Hadoop platform using MapReduce programming model. We use transformed database to reduce scanning time and directed graph to optimize the memory storage. Mapper is used to construct prefix and suffix projected databases for each length-1 frequent item parallel. The Reducer combines all intermediary outcomes to get final sequential patterns. Experiment results are compared against UDDAG, different values of minimum support, different massive data sets and with and without Hadoop platform which improves the scaling and speed performances. Experimental results show that DSPM using Hadoop MapReduce solves the scaling problem as well as storage problem of UDDAG. DOI: 10.17762/ijritcc2321-8169.15020

A neural network for mining large volumes of time series data

Efficiently mining large volumes of time series data is amongst the most challenging problems that are fundamental in many fields such as industrial process monitoring, medical data analysis and business forecasting. This paper discusses a high-performance neural network for mining large time series data set and some practical issues on time series data mining. Examples of how this technology is used to search the engine data within a major UK eScience Grid project (DAME) for supporting the maintenance of Rolls-Royce aero-engine are presented

Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets

Finding groups in data: Cluster analysis with ants

Wepresent in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This approach mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine, and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. As a case study, this paper focus on the behavior of clustering procedures in those new approaches. The proposed algorithm and its modifications are evaluated in a number of well-known benchmark datasets. Empirical results clearly show that ant-based clustering algorithms performs well when compared to another techniques