A5061449848- Data Mining Algorithms and Applications
- Data Management and Algorithms
- Advanced Database Systems and Queries
- Time Series Analysis and Forecasting
- Algorithms and Data Compression
- Rough Sets and Fuzzy Logic
- Gaze Tracking and Assistive Technology
- Tactile and Sensory Interactions
- Fuzzy Logic and Control Systems
- Hand Gesture Recognition Systems
Goa University
2024
University of Aizu
2021-2023
National Institute of Information and Communications Technology
2023
Rajiv Gandhi University of Knowledge Technologies
2020-2021
International Institute of Information Technology, Hyderabad
2020
Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance many real-world applications. Though several algorithms were described the literature to tackle pattern mining, most these use traditional horizontal (or row) database layout, that is, either they need scan times or do not allow asynchronous computation patterns. As result, this kind layout makes for discovering both time and memory inefficient. One cannot ignore mining data stored...
Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors a temporal database. Most previous studies focused on finding these row (temporal) databases and disregarded the occurrences of columnar databases. Furthermore, naïve approach transforming database into then applying existing algorithms to find interesting not practicable due computational reasons. With this...
A geo-referenced time series database represents the data generated by a set of fixed locations (or spatial items) observing particular phenomenon over time. This hides valuable information that can help users progress in their social and economic lives. paper presents new model Geo-referenced Periodic-Frequent Patterns (GPFPs) might be these databases. GPFP is frequently occurring items close to each other seen at regular intervals. Three constraints have been used figure out how...
Periodic-frequent pattern mining (PFPM) is an important data model having many real-world applications. However, this model's prosperous industrial use has been hindered by the problem of combinatorial explosion patterns, which generation too redundant most may be useless to user. We propose a novel closed periodic-frequent patterns that exist in temporal database address problem. Closed represent concise lossless subset uniquely preserves complete information all database. An efficient...
Finding periodic-frequent patterns in temporal databases is a prominent data mining problem with bountiful applications. It involves discovering all database that satisfy the user-specified minimum support ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min_sup</i> ) and maximum periodicity xmlns:xlink="http://www.w3.org/1999/xlink">max_per</i> constraints. xmlns:xlink="http://www.w3.org/1999/xlink">Min_sup</i> controls least number of...
Partial periodic-frequent pattern mining is an important knowledge discovery technique in data mining. It involves identifying all frequent patterns that have exhibited partial periodic behavior a temporal database. The following two limitations hindered the successful industrial application of this technique: (i) there exists no algorithm to find desired columnar databases, and (ii) existing algorithms are computationally expensive both terms runtime memory consumption. This paper tackles...
Partial periodic patterns are an important class of regularities in multiple time series data. Most previous works focused on finding these a binary by disregarding the quantities objects. This paper explores concept "fuzzy sets" and proposes novel model fuzzy partial (F3Ps) that may exist quantitative series. F3Ps have value because they represent predictable Unfortunately, is challenging due to its colossal search space. We introduce pruning technique reduce space computational cost...
Crucial information that can empower the users to achieve socioeconomic development lies hidden in big temporal databases. Previous studies employed partial periodic pattern mining techniques find all regularly occurring patterns data. Unfortunately, these consumes too much energy as they often produce many most of which may be uninteresting user. This paper tackles this problem by introducing two new types patterns, namely maximal and closed represent a concise set exist Two depth-first...