مادة آلی محلول رنگی (CDOM) مقیاس مهمی در سنجش کیفیت آب است. می‌توان اذعان کرد CDOM نور موجود لایه‌های را کاهش می‌دهد و فعالیت‌های بیولوژیکی فتوسنتز مختل می‌کند سرانجام، مانع رشد جمعیت فیتوپلانکتون‌‌هایی می‌شود که برای زنجیرة غذای آبزیان ضروری‌اند. بیشتر تحقیق‌هایی تا کنون به انجام رسیده، از طول موج خاصی اندازه‌گیری ضریب جذب استفاده شده است اما، این تحقیق، امکان گسترة‌ وسیعی باندهای طیف مرئی، تع...

The intelligent LINEX k-means clustering is a generalization of the k-means clustering so that the number of clusters and their related centroid can be determined while the LINEX loss function is considered as the dissimilarity measure. Therefore, the selection of the centers in each cluster is not randomly. Choosing the LINEX dissimilarity measure helps the researcher to overestimate or undere...

identifying clusters or clustering is an important aspect of data analysis. it is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. it is a main task of exploratory data mining, and a common technique for statistical data analysis this paper proposed an improved version of k-means algorithm, namely persistent k...

Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. Even though datasets have grown in size, the K-means algorithm remains as one of the most popular clustering methods, in spite of its dependency on the initial settings and high computational cost, especially in terms of d...

This paper shows that one can be competitive with the kmeans objective while operating online. In this model, the algorithm receives vectors v1, . . . , vn one by one in an arbitrary order. For each vector vt the algorithm outputs a cluster identifier before receiving vt+1. Our online algorithm generates Õ(k) clusters whose k-means cost is Õ(W ∗) where W ∗ is the optimal k-means cost using k cl...

Over half a century old and showing no signs of aging, k-means remains one of the most popular data processing algorithms. As is well-known, a proper initialization of k-means is crucial for obtaining a good final solution. The recently proposed k-means++ initialization algorithm achieves this, obtaining an initial set of centers that is provably close to the optimum solution. A major downside ...

We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming setting. We make no assumptions about the data, and our algorithm is very light-weight in terms of memory, and computation. This setting is applicable to unsupervised learning on massive data sets, or resource-constrained devices. The two main ingredients of our theoretical work are: ...