روش نوين خوشه‌بندي تركيبي با استفاده از سيستم ايمني مصنوعي و سلسله مراتبي

New Clustering Technique using Artificial Immune System and Hierarchical technique

سيستم ايمني مصنوعي (AIS) يكي از مهمترين الگوريتم‌هاي متاهيوريستيك به منظور حل مسائل بسيار پيچيده مي‌باشد. از اين الگوريتم مي‌توان در تحليل خوشه‌بندي داده‌ها استفاده نمود. علي‌رغم اينكه AIS قادر است پيكربندي فضاي جستجو را به خوبي نمايش دهد اما تعيين خوشه‌هاي داده‌ها به طور مستقيم با استفاده از خروجي آن بسيار مشكل است. بر اين اساس در اين مقاله الگوريتم دو مرحله‌اي پيشنهاد شده است. در مرحله اول با استفاده از الگوريتم AIS پيشنهادي، فضاي جستجو مورد بررسي قرار گرفته و پيكربندي فضا تعيين مي‌شود و در مرحله دوم با استفاده از روش خوشه‌بندي سلسله ‌مراتبي، خوشه‌ها و تعداد آنها مشخص مي‌شود. در انتها الگوريتم پيشنهادي بر روي نمونه واقعي متشكل از داده‌هاي زلزله در ايران پياده‌سازي و با نتايج الگوريتم مشابه مقايسه شده است. نتايج نشان داد كه الگوريتم پيشنهادي توانسته است نقايص موجود در AIS و روش خوشه‌بندي سلسله مراتبي را پوشش دهد و از طرفي از دقت و سرعت قابل قبولي برخوردار است.

Artificial immune system (AIS) is one of the most meta-heuristic algorithms to solve complex problems. With a large number of data, creating a rapid decision and stable results are the most challenging tasks due to the rapid variation in real world. Clustering technique is a possible solution for overcoming these problems. The goal of clustering analysis is to group similar objects. AIS algorithm can be used in data clustering analysis. Although AIS is able to good display configure of the search space, but determination of clusters of data set directly using the AIS output will be very difficult and costly. Accordingly, in this paper a two-step algorithm is proposed based on AIS algorithm and hierarchical clustering technique. High execution speed and no need to specify the number of clusters are the benefits of the hierarchical clustering technique. But this technique is sensitive to outlier data. So, in the first stage of introduced algorithm the search space and the configuration space are identified using the proposed AIS algorithm, and therefore outlier data are determined. Then in second phase, using hierarchical clustering technique, clusters and their number are determined. Consequently, the first stage of proposed algorithm eliminates the disadvantages of the hierarchical clustering technique, and AIS problems will be resolved in the second stage of the proposed algorithm. In this paper, the proposed algorithm is evaluated and assessed through two metrics that were identified as (i) execution time (ii) Sum of Squared Error (SSE): the average total distance between the center of a cluster with cluster members used to measure the goodness of a clustering structure. Finally, the proposed algorithm has been implemented on a real sample data composed of the earthquake in Iran and has been compared with the similar algorithm titled Improved Ant System-based Clustering algorithm (IASC). IASC is based on Ant Colony System (ACS) as the meta-heuristics clustering algorithm. It is a fast algorithm and is suitable for dynamic environments. Table 1 shows the results of evaluation. The results showed that the proposed algorithm is able to cover the drawbacks in AIS and hierarchical clustering techniques and on the other hand has high precision and acceptable run speed.