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    Mmulcriapp: ML and MCDA based approach for energy efficient communication for wsn based resource constrained iot devices
    (IEEE, 2025-05) Haribabu, K.
    Wireless Sensor Networks (WSNs) play a crucial role in various domains like environmental monitoring, agriculture, home automation, and healthcare. However, they face challenges such as limited resources, dynamic environments, data routing issues, scalability, unreliable wireless communication, mobility, security concerns, limited bandwidth, and fault tolerance. Machine Learning (ML) techniques have been utilized to address these challenges. Additionally, Multi Criteria Decision Analysis (MCDA), a tool for making decisions involving multiple criteria, is helpful in scenarios like cluster head selection in WSNs. This paper proposes a hybrid approach that combines ML for initial rounds, followed by MCDA based mechanisms in later rounds. The approach is evaluated using metrics like energy consumption, node degree, remaining energy, sink node location, and distance metrics and shows better performance compared to the ML technique alone.
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    A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering Algorithms
    (Springer, 2024-06) Challa, Jagat Sesh; Balasubramaniam, Sundar; Goyal, Navneet; Goyal, Poonam
    The advent of Big Data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as MPI, MapReduce, and Spark. An important step for any parallel clustering algorithm is the distribution of data amongst the cluster nodes. This step governs the methodology and performance of the entire algorithm. Researchers typically use random, or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning, as per the requirements of the algorithm. However, these strategies are generic and are not tailor-made for any specific parallel clustering algorithm. In this paper, we give a very comprehensive literature survey of MPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they employ. We also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS, Cell-Based Dimensional Split for dGridSLINK, which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution, and Projection-Based Split, which is a generic distribution strategy. All of these preserve spatial locality, achieve disjoint partitioning, and ensure good data load balancing. The experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for, based on which we give appropriate recommendations for their usage.
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    An Efficient Density Based Incremental Clustering Algorithm in Data Warehousing Environment
    (IPCSIT, 2009) Goyal, Navneet; Goyal, Poonam
    Data Warehouses are a good source of data for downstream data mining applications. New data arrives in data warehouses during the periodic refresh cycles. Appending of data on existing data requires that all patterns discovered earlier using various data mining algorithms are updated with each refresh. In this paper, we present an incremental density based clustering algorithm. Incremental DBSCAN is an existing incremental algorithm in which data can be added/deleted to/from existing clusters, one point at a time. Our algorithm is capable of adding points in bulk to existing set of clusters. In this new algorithm, the data points to be added are first clustered using the DBSCAN algorithm and then these new clusters are merged with existing clusters, to come up with the modified set of clusters. That is, we add the clusters incrementally rather than adding points incrementally. It is found that the proposed incremental clustering algorithm produces the same clusters as obtained by Incremental DBSCAN. We have used R*-trees as the data structure to hold the multidimensional data that we need to cluster. One of the major advantages of the proposed approach is that it allows us to see the clustering patterns of the new data along with the existing clustering patterns. Moreover, we can see the merged clusters as well. The proposed algorithm is capable of considerable savings, in terms of region queries performed, as compared to incremental DBSCAN. Results are presented to support the claim
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    A Multi-purpose Density Based Clustering Framework
    (Springer, 2011) Goyal, Navneet; Goyal, Poonam
    In this paper, we present a multi-purpose density-based clustering framework. The framework is based on a novel cluster merging algorithm which can efficiently merge two sets of DBSCAN clusters using the concept of intersection points. It is necessary and sufficient to process just the intersection points to merge clusters correctly. The framework allows for clustering data incrementally, parallelizing the DBSCAN algorithm for clustering large data sets and can be extended for clustering streaming data. The framework allows us to see the clustering patterns of the new data points separately. Results presented in the paper establish the efficiency of the proposed incremental clustering algorithm in comparison to IncrementalDBSCAN algorithm. Our incremental algorithm is capable of adding points in bulk, whereas IncrementalDBSCAN adds points, one at a time.