Efficient binary embedding of categorical data using BinSketch

Verma, Bhisham Dev; Pratap, Rameshwar; Bera, Debajyoti

doi:10.1007/s10618-021-00815-y

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…The Hamming distance has several limitations. First, it does not distinguish between different types of differences [16] such as Hamming distance of 1 is the same whether the two vectors differ in the first or last positions. Second, the Hamming distance does not consider the order of the categories [17].…”

Section: Related Workmentioning

confidence: 99%

Clustering performance using k-modes with modified entropy measure for breast cancer

Mahfuz,

Suhartanto,

Kusmardi

et al. 2023

IJEECS

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<span>Breast cancer is a serious disease that requires data analysis for diagnosis and treatment. Clustering is a data mining technique that is often used in breast cancer research to assess the level of malignancy at an early stage. However, clustering categorical data can be challenging because different levels in categorical variables can impact the clustering process. This research proposes a modified entropy measure (MEM) to enhance clustering performance. MEM aims to address the issue of distance-based measures in clustering categorical data. It is also a useful tool for assessing data loss in categorical clustering, which helps to understand the patterns and relationships by quantifying the information lost during clustering. An evaluation compares k-modes+MEM, k-means+MEM, DBSCAN+MEM, and affinity+MEM with conventional clustering algorithms. The assessment metrics of clustering accuracy, intra-cluster distance and fowlkes-mallow index (FMI) are employed to evaluate the algorithm performance. Experimental results show significant improvements. k-Modes+MEM algorithm achieves a reduction in average intra-cluster distance and outperforms other algorithms in accuracy, intra-cluster distance, and FMI. The proposed algorithm can be extended to heterogeneous datasets in various domains such as healthcare, finance, and marketing.</span>

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Section: Related Workmentioning

confidence: 99%

Clustering performance using k-modes with modified entropy measure for breast cancer

Mahfuz,

Suhartanto,

Kusmardi

et al. 2023

IJEECS

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An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors

Bruch

Nardini

Ingber³

et al. 2023

ACM Trans. Inf. Syst.

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Maximum Inner Product Search or top- k retrieval on sparse vectors is well-understood in information retrieval, with a number of mature algorithms that solve it exactly. However, all existing algorithms are tailored to text and frequency-based similarity measures. To achieve optimal memory footprint and query latency, they rely on the near stationarity of documents and on laws governing natural languages. We consider, instead, a setup in which collections are streaming—necessitating dynamic indexing—and where indexing and retrieval must work with arbitrarily distributed real-valued vectors. As we show, existing algorithms are no longer competitive in this setup, even against naïve solutions. We investigate this gap and present a novel approximate solution, called Sinnamon , that can efficiently retrieve the top- k results for sparse real valued vectors drawn from arbitrary distributions. Notably, Sinnamon offers levers to trade-off memory consumption, latency, and accuracy, making the algorithm suitable for constrained applications and systems. We give theoretical results on the error introduced by the approximate nature of the algorithm, and present an empirical evaluation of its performance on two hardware platforms and synthetic and real-valued datasets. We conclude by laying out concrete directions for future research on this general top- k retrieval problem over sparse vectors.

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Bridging Dense and Sparse Maximum Inner Product Search

Bruch,

Nardini,

Ingber

et al. 2024

ACM Trans. Inf. Syst.

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Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top- \(k\) retrieval in Information Retrieval. This duality exists because sparse and dense vectors serve different end goals. That is despite the fact that they are manifestations of the same mathematical problem. In this work, we ask if algorithms for dense vectors could be applied effectively to sparse vectors, particularly those that violate the assumptions underlying top- \(k\) retrieval methods. We study clustering-based approximate MIPS where vectors are partitioned into clusters and only a fraction of clusters are searched during retrieval. We conduct a comprehensive analysis of dimensionality reduction for sparse vectors, and examine standard and spherical KMeans for partitioning. Our experiments demonstrate that clustering-based retrieval serves as an efficient solution for sparse MIPS. As byproducts, we identify two research opportunities and explore their potential. First, we cast the clustering-based paradigm as dynamic pruning and turn that insight into a novel organization of the inverted index for approximate MIPS over general sparse vectors. Second, we offer a unified regime for MIPS over vectors that have dense and sparse subspaces, that is robust to query distributions.

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Efficient binary embedding of categorical data using BinSketch

Cited by 3 publications

References 28 publications

Clustering performance using k-modes with modified entropy measure for breast cancer

Clustering performance using k-modes with modified entropy measure for breast cancer

An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors

Bridging Dense and Sparse Maximum Inner Product Search

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