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Latent Dirichlet allocation (LDA) is a popular method for analyzing large text corpora, but it suffers from instability due to its reliance on random initialization. This results in different outcomes for replicated runs, hindering reproducibility. To address this, we introduce LDAPrototype, a new approach for selecting the most representative LDA run from multiple replications on the same dataset. LDAPrototype enhances the reliability of LDA conclusions by ensuring greater similarity between replications compared to traditional LDA runs or models chosen based on perplexity or NPMI. A key feature of LDAPrototype is its use of a novel model similarity measure called S-CLOP (Similarity of multiple sets by Clustering with LOcal Pruning). It is based on topic similarities, for which we compare the usage of measures like the thresholded Jaccard coefficient, cosine similarity, Jensen-Shannon divergence, and rank-biased overlap. The effectiveness of LDAPrototype is demonstrated through its application to six real datasets, including newspaper articles and tweets. The results show improved reproducibility and reliability in topic modeling outcomes. LDAPrototype's approach is noteworthy for its practical applicability, comprehensibility, ease of implementation, and computational efficiency. Furthermore, the algorithm's concept can be generalized to other topic modeling procedures that characterize topics through word distributions, making it a versatile tool in text data analysis. [ABSTRACT FROM AUTHOR]

We consider two problems that are close in terms of formulation. The first one is that of clustering, i.e., partitioning the set of -dimensional Euclidean vectors into a given number of clusters with different types of centers so that the total variance would be minimum. Here, by variance we mean the sum of squared distances between the elements of the clusters and their centers. There are three types of centers: an arbitrary point (clearly, the centroid is the best choice), a point of the initial set (the so-called medoid), or a fixed point of the space given in advance.. The sizes of the clusters are also given as part of the input. The second problem under consideration is the problem of choosing a subset of vectors of fixed cardinality having the minimum sum of squared distances between its elements and the centroid. Polynomial-time approximation schemes (PTAS) are constructed for each of these problems. [ABSTRACT FROM AUTHOR]

We consider the following 2-clustering problem. Given N points in Euclidean space, partition it into two subsets (clusters) so that the sum of squared distances between the elements of the clusters and their centers would be minimum. The center of the first cluster coincides with its centroid (mean) while the center of the second cluster should be chosen from the set of the initial points (medoid). It is known that this problem is NP-hard if the cardinalities of the clusters are given as a part of the input. In this paper we prove that the problem remains NP-hard in the case of arbitrary clusters sizes and suggest a 2-approximation polynomial-time algorithm for this problem. [ABSTRACT FROM AUTHOR]

A silhouette index is a well-known measure of an internal criteria validation for the clustering algorithm results. While it is a medoid-based validation index, a centroid-based validation index that is called a centroid-based shadow value (CSV) has been developed. Although both are similar, the CSV has an additional unique property where an image of a 2-dimensional neighborhood graph is possible. A new internal validation index is proposed in this article in order to create a medoid-based validation that has an ability to visualize the results in a 2-dimensional plot. The proposed index behaves similarly to the silhouette index and produces a network visualization, which is comparable to the neighborhood graph of the CSV. The network visualization has a multiplicative parameter (c) to adjust its edges visibility. Due to the medoid-based, in addition, it is more an appropriate visualization technique for any type of data than a neighborhood graph of the CSV. [ABSTRACT FROM AUTHOR]

Persistent cloud cover is an important obstacle to studying the ground surface of tropical rain forest areas using high-resolution optical data, such as those obtained with Landsat satellites. The identification and masking of the cloud-affected parts of such images is a necessary preprocessing step, but it easily leads to impractical fragmentation of the informative image area. Pixel-based multitemporal image compositing solves the fragmentation problem, but depends on a predefined compositing period length and compositing criterion. Here, we evaluate the radiometric consistency of Landsat TM/ETM+ composite images over undisturbed Amazonian forests and test to what degree it varies with the number of available multitemporal observations per pixel and the compositing criterion. Five compositing criteria were tested: maximum NDVI, median red, median near-infrared, multidimensional medoid, and minimum aerosol optical thickness. Each was applied to datasets consisting of 3–30 observations per pixel. Compositing quality was assessed both visually and with quantitative measures using the overlap area of neighboring WRS-2 scenes. We found that the medoid approach generated the most radiometrically consistent composite images. Composite image quality increased monotonically with the number of observations, but with diminishing returns. Satisfactory results were generally obtained with 10–15 observations per pixel. [ABSTRACT FROM PUBLISHER]

The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful-compared to classical centroid-based techniques-when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

Multi-temporal satellite imagery can be composited over a season (or other time period) to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. For the purposes of vegetation monitoring, a commonly used technique is the Maximum NDVI Composite, used in conjunction with variety of other constraints. The current paper proposes an alternative based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values, and appears to be better at producing imagery which is representative of the time period. For each pixel, the medoid is always selected from the available dates, so the result is always a single observation for that pixel, thus preserving relationships between bands. The method is applied to Landsat TM/ETM+ imagery to create seasonal reflectance images (four per year), with the aim being a regular time series of reflectance values which captures the variability at seasonal time scales. Analysis of the seasonal reflectance values suggests that resulting temporal image composites are more representative of the time series than the maximum NDVI seasonal composite. [ABSTRACT FROM AUTHOR]

Clustering remains one of the most difficult challenges in data mining. This paper proposes a new algorithm, CLAM, using a hybrid metaheuristic between VNS and Tabu Search to solve the problem of k-medoid clustering. The new technique is compared to the well-known CLARANS. Experimental results show that, given the same computation times, CLAM is more effective. [ABSTRACT FROM AUTHOR]

Averaging GPS trajectories is needed in applications such as clustering and automatic extraction of road segments. Calculating mean for trajectories and other time series data is non-trivial and shown to be an NP-hard problem. medoid has therefore been widely used as a practical alternative and because of its (assumed) better noise tolerance. In this paper, we study the usefulness of the medoid to solve the averaging problem with ten different trajectory-similarity/-distance measures. Our results show that the accuracy of medoid depends mainly on the sample size. Compared to other averaging methods, the performance deteriorates especially when there are only few samples from which the medoid must be selected. Another weakness is that medoid inherits properties such as the sample frequency of the arbitrarily selected sample. The choice of the trajectory distance function becomes less significant. For practical applications, other averaging methods than medoid seem a better alternative for higher accuracy. [ABSTRACT FROM AUTHOR]

Tertiary structure (3D) is the physical context of RNA regulatory activity. Retroviruses are RNA viruses that replicate through the proviral DNA intermediate transcribed by hosts. Proviral transcripts form inhomogeneous populations due to variable structural ensembles of overlapping regulatory RNA motifs in the 5′-untranslated region (UTR), which drive RNAs to be spliced or translated, and/or dimerized and packaged into virions. Genetic studies and structural techniques have provided fundamental input constraints to begin predicting HIV 3D conformations in silico. Using SimRNA and sets of experimentally-determined input constraints of HIVNL4-3 trans-activation responsive sequence (TAR) and pairings of unique-5′ (U5) with dimerization (DIS) or AUG motifs, we calculated a series of 3D models that differ in proximity of 5′-Cap and the junction of TAR and PolyA helices; configuration of primer binding site (PBS)-segment; and two host cofactors binding sites. Input constraints on U5-AUG pairings were most compatible with intramolecular folding of 5′-UTR motifs in energetic minima. Introducing theoretical constraints predicted metastable PolyA region drives orientation of 5′-Cap with TAR, U5 and PBS-segment helices. SimRNA and the workflow developed herein provides viable options to predict 3D conformations of inhomogeneous populations of large RNAs that have been intractable to conventional ensemble methods. [ABSTRACT FROM AUTHOR]

Summary: Gene expression programming (GEP) algorithm is one of the most effective function mining algorithms in enabling the mathematical equation fitting for the input dataset. However, GEP algorithm encounters low efficiency issue in big data processing due to large overhead in its evolution when it handles the large‐scale data. In order to solve the issue, this paper presents two parallelized GEP algorithms using MapReduce. Based on data separation, the first algorithm aims at speeding up the large‐scale classification. However, it is lack of ability to output the mined equation explicitly. Therefore, based on the further improvements of the first algorithm, the second parallelized GEP algorithm aims at mining the equation efficiently and also outputs the equation explicitly and directly. The experimental results show that both algorithms are effective for processing large volume of data. [ABSTRACT FROM AUTHOR]