Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model

Abstract

EPs (Extracting Frequent Patterns) from the continuous transactional data streams is a challenging and critical task in some of the applications, such as web mining, data analysis and retail market, prediction and network monitoring, or analysis of stock market exchange data. Many algorithms have been developed previously for mining FPs (Frequent Patterns) from a data stream. Such algorithms are currently highly required to develop new solutions and approaches to the precise handling of data streams. New techniques, solutions, or approaches are developed to address unbounded, ordered, and continuous sequences of data and for the generation of data at a rapid speed from data streams. Hence, extracting FPs using fresh or recent data involves the high-level analysis of data streams. We have suggested an efficient technique for the window sliding model; this technique extracts new and fresh FPs from high-speed data streams. In this study, a CPILT (Compacted Tree Compact Pattern Tree) is developed to capture the latest contents in the stream and to efficiently remove outdated contents from the data stream. The main concept introduced in this work on CPILT is the dynamic restructuring of a tree, which is helpful in producing a compacted tree and the frequency descending structure of a tree on runtime. With the help of the mining technique of FP growth, a complete list of new and fresh FPs is obtained from a CPILT using an existing window. The memory usage and time complexity of the latest FPs in high-speed data streams can efficiently be determined through proper experimentation and analysis. Therefore, the tracks of the last visited batches are maintained via FPM, and an extra pointer is used for every node to count the last restructured batch number.Frequency list contents are changed for SW reflection using nodes. The FP-growth technique is utilized for mining when the information is captured using SW [7], that is, the FPs from a complete information sets.However, FPM has several limitations. First, information or item sets are stored in the canonical order, and using Hence, the updating process for a frequency list related to specified nodes is not performed, given new incoming batches that are not visited. For the current window, FPM may leave some invalid nodes during updating. Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model MehranHence, for mining, FPM consumes more time than such types of trees based on structures organized in such a way that the frequency depends on the order of items.Moreover, FPM construction is totally based on an assumption that does not consider the limitations of the main memory, which is unrealistic in considering or processing huge amounts of data, such as a data stream.By contrast, CPILT, the proposed tree in this study, exactly provides similar information on data streams and performs similarly to FPM, such action with the storage only in an FP tree with a strong compact structure, thereby presenting an efficient...

Comparative analysis of TF-IDF and loglikelihood method for keywords extraction of twitter data

“…In this paper, the Term Frequency-Inverse Document Frequency (TF-IDF) [11] and Loglikelihood [12] methods are used for keywords extraction. This research essentially focused on the analytical analysis of both methods and evaluates [13] which method is more suitable for keywords extractions. This rest of the paper is illustrated as follows: Section II describes some related work which incorporates the techniques associated with the extraction of keywords.…”

Computer Simulation Based Optimization of Aspect Ratio for Micro and Nanochannels

“…Previously and presently, all the researchers working on CFD have done the ANSYS simulations on individual element [24][25]. The usage of a computer to characterize the fluid dynamics in micro/nanochannels and in other fields is wide spread [26][27][28][29].…”