SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Data Mining
نویسندگان
چکیده
منابع مشابه
SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Data Mining
In GIS or Geographic Information system technology, a vast volume of spatial data has been accumulated, thereby incurring the necessity of spatial data mining techniques. Displaying and visualizing such data items are important aspects. But no RDBMS software is loaded with displaying the spatial result over a MAP overlay or answer spatial queries like “all the points within” certain Neighborhoo...
متن کاملAK Miner: An Online Data Mining Tool
The world today is moving faster in the software industry than anywhere else. To keep data mining tools accessible to everyone is a Herculean task. We plan to solve this problem in this paper and tool. AK Miner is an online data mining tool that is easily accessible by anyone with a computer. The one of the advantages of AK Miner over the already preexisting Data mining Tools is that it is not ...
متن کاملData-Mining-Driven Neighborhood Search
Metaheuristic approaches based on neighborhood search escape local optimality by applying predefined rules and constraints, such as tabu restrictions (in tabu search), acceptance criteria (in simulated annealing), and shaking (in VNS). We propose a general approach that attempts to learn (offline) the guiding constraints that, when applied online, will result in effective escape directions from...
متن کاملLearning FCM by Data Mining in a Purchase System
Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrat...
متن کاملMax-Miner Algorithm Using Knowledge Discovery Process in Data Mining
Discovering frequent item sets is an important key problem in data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. Typical algorithms for solving this problem operate in a bottom-up, breadth-first search direction. The computation starts from frequent itemsets (the minimum length frequent itemsets) and continues until all maximal (lengt...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IOSR Journal of Computer Engineering
سال: 2012
ISSN: 2278-8727,2278-0661
DOI: 10.9790/0661-0460105