Tree-Mining: Understanding Applications and Challenges

Tree-mining is an essential system of techniques and software technologies for multi-level and multi-angled operations in databases. Pertaining to the purview of this manuscript, several applications of various sub-techniques of tree mining have been explored. The current write-up is aimed at investigating the major applications and challenges of different types and techniques of tree mining, as there have been patchy and scanty investigations so far in this context. To accomplish these tasks, the author has reviewed some of the latest and most pertinent research articles of the last two decades to investigate the titled aspects of this technique

A Novel Frequent Pattern Mining Algorithm for Evaluating Applicability of a Mobile Learning Framework

The applicability of a mobile learning system reflects how it works in an actual situation under diverse conditions In previous studies researches for evaluating applicability in learning systems using data mining approaches are challenging to find The main objective of this study is to evaluate the applicability of the proposed mobile learning framework This framework consists of seven independent variables and their influencing factors Initially 1000 students and teachers were allowed to use the mobile learning system developed based on the proposed mobile learning framework The authors implemented the system using Moodle mobile learning environment and used its transaction log file for evaluation Transactional records that were generated due to various user activities with the facilities integrated into the system were extracted These activities were classified under eight different features i e chat forum quiz assignment book video game and app usage in thousand transactional rows A novel pattern mining algorithm namely Binary Total for Pattern Mining BTPM was developed using the above transactional dataset s binary incidence matrix format to test the system applicability Similarly Apriori frequent itemsets mining and Frequent Pattern FP Growth mining algorithms were applied to the same dataset to predict system applicabilit

Sampling Algorithms for Evolving Datasets

Perhaps the most flexible synopsis of a database is a uniform random sample of the data; such samples are widely used to speed up the processing of analytic queries and data-mining tasks, to enhance query optimization, and to facilitate information integration. Most of the existing work on database sampling focuses on how to create or exploit a random sample of a static database, that is, a database that does not change over time. The assumption of a static database, however, severely limits the applicability of these techniques in practice, where data is often not static but continuously evolving. In order to maintain the statistical validity of the sample, any changes to the database have to be appropriately reflected in the sample. In this thesis, we study efficient methods for incrementally maintaining a uniform random sample of the items in a dataset in the presence of an arbitrary sequence of insertions, updates, and deletions. We consider instances of the maintenance problem that arise when sampling from an evolving set, from an evolving multiset, from the distinct items in an evolving multiset, or from a sliding window over a data stream. Our algorithms completely avoid any accesses to the base data and can be several orders of magnitude faster than algorithms that do rely on such expensive accesses. The improved efficiency of our algorithms comes at virtually no cost: the resulting samples are provably uniform and only a small amount of auxiliary information is associated with the sample. We show that the auxiliary information not only facilitates efficient maintenance, but it can also be exploited to derive unbiased, low-variance estimators for counts, sums, averages, and the number of distinct items in the underlying dataset. In addition to sample maintenance, we discuss methods that greatly improve the flexibility of random sampling from a system's point of view. More specifically, we initiate the study of algorithms that resize a random sample upwards or downwards. Our resizing algorithms can be exploited to dynamically control the size of the sample when the dataset grows or shrinks; they facilitate resource management and help to avoid under- or oversized samples. Furthermore, in large-scale databases with data being distributed across several remote locations, it is usually infeasible to reconstruct the entire dataset for the purpose of sampling. To address this problem, we provide efficient algorithms that directly combine the local samples maintained at each location into a sample of the global dataset. We also consider a more general problem, where the global dataset is defined as an arbitrary set or multiset expression involving the local datasets, and provide efficient solutions based on hashing

Portland Daily Press: February 14,1880

https://digitalmaine.com/pdp_1880/1136/thumbnail.jp

Portland Daily Press: February 14,1880

https://digitalmaine.com/pdp_1880/1036/thumbnail.jp