By Mani Abedini, Michael Kirley (auth.), Dianhui Wang, Mark Reynolds (eds.)
This ebook constitutes the refereed court cases of the twenty fourth Australasian Joint convention on synthetic Intelligence, AI 2011, held in Perth, Australia, in December 2011. The eighty two revised complete papers provided have been rigorously reviewed and chosen from 193 submissions. The papers are geared up in topical sections on info mining and data discovery, computing device studying, evolutionary computation and optimization, clever agent platforms, good judgment and reasoning, imaginative and prescient and photos, photo processing, traditional language processing, cognitive modeling and simulation know-how, and AI applications.
Read or Download AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings PDF
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Extra resources for AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings
Step 4: We perform k-Means algorithm on the reduced time series using the initial centroids obtained in Step 2. Fig. 2. The algorithm for the proposed clustering approach To have an efficient implementation of our proposed clustering approach, we try to speed up Step 1 and Step 2. To make the brute-force algorithm for finding 1-motif more efficiently in a clustering context, we apply some state-of-the-art techniques to improve it. Furthermore, we devise another technique to derive the initial centroids from the results of k-Means clustering on motifs.
Using the 10 time-series as seeds, we produced variation of the original patterns by adding small time shifting (2-3 % of the series length), and interpolated Gaussian noise. Gaussian noisy peaks are interpolated using splines to create smooth random variations. Experimental Results For the Heterogeneous dataset we tested on 1000 time series. In this dataset, each time series consists of 1024 points. 008. Here, we applied PAA as a feature extraction method with the length of each segment l = 8.
K-Means/PAA+Motif in terms of the number of iterations over different lengths of time series (Heterogeneous dataset) Since motif discovery is an important task in time series data mining, this task should be included in any time series data mining systems beside similarity search, classification, clustering, rule discovery, prediction and anomaly detection. In real world applications, motif discovery helps to provide the most representative pattern or a good summary about a time series. Therefore, it is a task that should be done before several other time series data mining tasks such as classification, clustering, rule Motif-Based Method for Iniitialization the K-Means Clustering for Time Series Data 19 discovery, and prediction.
AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings by Mani Abedini, Michael Kirley (auth.), Dianhui Wang, Mark Reynolds (eds.)