Density based clustering algorithms (DBCLAs) rely on the notion of density to identify clusters of arbitrary shapes, sizes with varying densities. Existing surveys on DBCLAs cover only a selected set of algorithms. These surveys fail to provide an extensive information about a variety of DBCLAs proposed till date including a taxonomy of the algorithms. In this paper we present a comprehensive survey of various DBCLAs over last two decades along with their classification. We group the DBCLAs in each of the four categories: density definition, parameter sensitivity, execution mode and nature of data and further divide them into various classes under each of these categories. In addition, we compare the DBCLAs through their common features and variations in citation and conceptual dependencies. We identify various application areas of DBCLAs in domains such as astronomy, earth sciences, molecular biology, geography, multimedia. Our survey also identifies probable future directions of DBCLAs where involvement of density based methods may lead to favorable results.

基于密度的聚类算法（DBCLA）依靠密度的概念来识别具有不同密度的任意形状，大小的簇。有关DBCLA的现有调查仅涵盖一组选定的算法。这些调查未能提供有关迄今为止提出的各种DBCLA的广泛信息，包括算法的分类法。在本文中，我们对过去20年中的各种DBCLA及其分类进行了全面的调查。我们将DBCLA分为四个类别：密度定义，参数敏感性，执行模式和数据性质并将它们进一步分为这些类别下的各个类别。另外，我们通过DBCLA的共同特征以及引文和概念上的依赖性的变化比较DBCLA。我们确定DBCLA在天文学，地球科学，分子生物学，地理，多媒体等领域的各种应用领域。我们的调查还确定了DBCLA未来的可能方向，其中基于密度的方法的参与可能会带来有利的结果。