dataesr/scientific-paragraphs-categorization huggingface dataset:
An improved AntBee called FractAntBee was proposed  [24]  that incorporated main characteristics of fractal theory. According to  [35] , a novel approach to image segmentation based on Ant Colony System (ACS) is proposed. In ACS algorithm an artificial ant colony is capable of solving the traveling salesman problem  [36]. As in ACO for the TSP, in ACO-based algorithms for clustering, each ant tries to find a cost-minimizing path, where the nodes of the path are the data points to be clustered. Like in the TSP, the cost of moving from data point   to   is the distance (, ) between these points, measured by some appropriate dissimilarity metric. Thus, the next point to be added to the path tends to be similar to the last point on the path. An important way in which these algorithms deviate from ACO algorithms is that the ants do not necessarily visit all data points  [37].
Proposed Methodology In ASClass algorithm, we have assumed that a graph  = { 1 ,. ,   } of  object has been collected by the domain expert where each object is a vector of  numerical values V 1 ,. , V. For measuring the similarity between objects we will use in the following Euclidean distance between two vectors, denoted by , which is used for edges in graph   [38]. The complete set of parameters of our model will be presented in Section 3. 3. Initially all the objects will be scattered randomly on the graph ; each node in the graph represents an object  in the datasets. The edge that connects two objects in the graph-data represents a measure of dissimilarity between these objects in the database. A class is represented by a route connecting a set of objects. In ASClass we chose to use more than one colony of ants, the number of colonies needed here is equal to the number of classes in the database.
Initially, for each colony,  artificial ants are placed on a selected object, called "nestobject. " For each cluster we randomly chose one and only one "nest-object. " The simulation model is detailed in Figure  1. The objective is to find the shortest route between the given objects and return to nest-object, while keeping in mind that each object can be connected to the path only once. The path traced by the ant represents a cluster in the dataset. Figure  2  shows a possible result of ASClass algorithm execution on the graph of Figure  1. It may be noted that, at the end of algorithm, each colony gives a collective traced-path that represent a cluster in the partition. For each colony, an artificial agent possesses three behavioral rules: (i) a movement item inspired by ant foraging behavior; (ii) an object fixing item inspired by the collective weaving in social spiders; (iii) a return to web item.
Movement Item. An artificial ant  is an agent which moves from an object  to an object  on a dataset graph. It   decides which object to reach among the objects it can access from its current position. The agent selects the object to move on according to a probability density   depending on the trail accumulated on edges and on the heuristic value, which was chosen here to be a dissimilarity measures. 𝑃 𝑘 𝑖𝑗 (𝑡) = [𝜏 𝑖𝑗 ] 𝛼 [𝜂 𝑖𝑗 ] 𝛽 ∑ 𝑙∈𝑁 𝑘 𝑖 [𝜏 𝑖𝑙 ] 𝛼 [𝜂 𝑖𝑙 ] 𝛽 if 𝑗 ∈ 𝑁 𝑘 𝑖. (2)  and  are two parameters which determine the relative influence of the pheromone trail and the dissimilarity measures and   is the set of objects which ant  has not yet connected to its tour.
This process is achieved for each colony. At the end of process we obtain  routes which represent the  clusters in the datasets. All notations used in ASClass algorithm are shown as follows:   (): the amount of pheromone on edge which connects city  to city  at time. : parameter that control the relative importance of the trail intensity. : parameter that control the visibility. : the visibility. : the coefficient of decay. : the number of objects forming the tour constructed by the ant. : set of objects which ant  has not yet connected to its tour. (): transition probability from object  to object. : scaling factor.
First Solution: ASClass i Algorithms. In order to find partition for unclassifiable objects (Outliers), we propose applying, respectively, -means algorithm, -medoid algorithm, and FCM algorithm on dataset of Outliers samples. Initialize randomly the  objects in a 2D environment Choose randomly the nest-object for each cluster, we assume  init this object. For each nest-object determine the cluster,  init ← ( selected ) End For For each cluster determine the number of objects ( init ) End For For all colony do For  = 1 to  max do  current ←  selected For all ants do Repeat  ←  current Choose in the list    (list of objects not visited) an object  according to the below formula: 𝑃 𝑘 𝑖𝑗 (𝑡) = { { { { { { { [𝜏 𝑖𝑗 ] 𝛼 [𝜂 𝑖𝑗 ] 𝛽 ∑ 𝑙∈𝐽 𝑘 𝑖 [𝜏 𝑖𝑙 ] 𝛼 [𝜂 𝑖𝑙 ] 𝛽 if 𝑗 ∈ 𝐽 𝑘 𝑖 0 if 𝑗 ∉ 𝐽 𝑘 𝑖 𝑂 current ← 𝑗 Until (𝑁 -𝐽 𝑘 𝑖 ) = 𝑁𝑏(𝐶 init ) Place a quantity of pheromone on the route according to the following equation: Δ𝜏 𝑘 𝑖𝑗 (𝑡) = { { { 𝑄 𝐿 𝑘 (𝑡) if edge (𝑖, 𝑗) belongs to 𝑇 𝑘 0 otherwise.
Artificial and Real Data. To evaluate the contribution of our method, we use several numerical datasets, including artificial and real databases from the Machine Learning Repository  [39]. Concerning the artificial datasets, the databases art1, art2, art3, art5, and art6 are generated with Gaussian laws and with various difficulties (classes overlay, nonrelevant attributes, etc. ), and art4 data is generated with uniform law. The general information about the databases is summarized in Table  2. For each data file, the following fields are given: the number of objects (), the number of attributes ( Att ), and the number of clusters expected to be found in the datasets (  ). Art1 dataset is the type of data most frequently used within previous work on ant based clustering algorithm  [22, 23].
The data are normalized in [0, 1], the measure of similarity is based on Euclidian distance, and the algorithms parameters used in our tests were always the same for all databases and all algorithms. As can be seen in Table  2 , Fisher's Iris dataset contains 3 classes of 50 instances where each class refers to a type of Iris plant. An example of class discovery of ASClass on Iris dataset is shown in Figure  7. Five versions of the ASClass algorithm were coded and tested with test data in order to determine accuracy or results. One implementation presented basic features of ASClass without improvements. ASClass 1 , ASClass 2 , and ASClass 3 are improvements versions of the basic ASClass in which we propose applying, respectively, the first solution described in Section 3. 4. 1. F-ASClass is a fuzzy-ant clustering solutions described in Section 3.
Evaluation Functions. The quality of the clustering results of the different algorithms on the test sets are compared using the following performances measures. The first performance measure is the error classification (Ec) index. It is defined as Ec = 1 - ∑ 𝐾 𝑘=1 Recall 𝑘 𝐾 , Recall 𝑘 = 𝑛 𝑘𝑘 𝑛 𝑘. ( 9 ) The second measure used in this paper is the accuracy coefficients. It determines the ratio of correctly assigned objects: Accuracy = ∑ 𝐾 𝑘=1 𝑛 𝑘𝑘 𝑁 , (10) where   is the number of objects of the th class correctly classified,   is the number of objects in cluster   , and  is the total number of objects in dataset. Not ass represents the percentage of not assigned objects in the predicted partition. Moreover, we will use separation index. It is defined in  [40]  as 𝑆 = ∑ 𝐾 𝑖=1 ∑ 𝑁 𝑗=1 (𝜇 𝑖𝑗 ) 2 󵄩 󵄩 󵄩 󵄩 󵄩 𝑂 𝑗 -𝜐 𝑖 󵄩 󵄩 󵄩 󵄩 󵄩 2 𝑁 * min 𝑖,𝑗 󵄩 󵄩 󵄩 󵄩 󵄩 𝑂 𝑗 -𝜐 𝑖 󵄩 󵄩 󵄩 󵄩 󵄩 2.
4. 3. Results. This section is divided into two parts. First, we study the behavior of ASClass with respect to the number of ants in each colony using Iris dataset. Second, we compare F-ASClass with the -means, -medoid, FCM, and ASClass i ( = 1, 2, 3), using test datasets presented in Table  2. This comparison can be done on the basis of average classification error and average value of accuracy.
As a semisupervised clustering algorithm, we can use confusion matrix as a visualization tool to evaluate the performance of ASClass. It contains information about the number per class of well classifies and mislabeled samples. Furthermore, it contains information about the capability of system to not mislabel one cluster as another. An example of confusion matrix on Iris dataset is shown in Figures  8(a )-8(g). It is important to note that Iris dataset presents the following characteristic: one cluster is linearly separable from the other two; the latter are not linearly separable from each other. We have presented this characteristic in Figure  7. Results presented in Figures  8(a )-8(g) prove that ASClass may be capable of identifying this characteristic. It is clear to see that for all examples in Figures  8(a )-8(g), the illustrated results show that all predicted samples of cluster 1 are correctly assigned as cluster 1.
Comparative Analysis. In order to compare the effectiveness of our proposed improvements of ASClass, we apply them to datasets presented in Table  2. We also use tree indexes of cluster validity: error classification (Ec), accuracy, and separation () coefficient. "The index should make good intuitive sense, should have a basis in theory, and should be readily computable"  [4]. Note that underline bold face indicates the best and bold face indicates the second best. In -means, -medoid, and FCM algorithms the predefined input number of clusters  is set to the known number of classes in each dataset. Tables  4  and 5  contain, respectively, the mean, standard deviations, mode, minimal, and maximal value of error rate of classification and the accuracy obtained by the four     In Tables  6  and 7 , we report, respectively, the mean, standard deviations, mode, minimal, and maximal values of the value of error rate of classification and accuracy obtained by the four automatic clustering algorithms (ASClass 1 , ASClass 2 , ASClass 3 , and F-ASClass).
The only exception is for Art3 and Pima datasets. The best values on error rate classification for these both datasets are given by ASClass 1. The best value for Moreover, F-ASClass appears to be the most stable algorithm by having the lowest value of standard deviation for all indexes validity on all datasets. Both ASClass 1 and ASClass 2 are unable to find new partition for Art4 dataset where the number of unclassified objects is too low (unclassified objects = 3). However both ASClass 3 and F-ASClass can be executed on small datasets. This feature proves that fuzzy-hybrid techniques are more efficient than hard-hybrid technique. Table  8  shows that the most partition is made by F-ASClass and the second best value is given by ASClass 3 ; thus a smallest  indeed indicates a valid optimal partition.
Conclusion We have presented in this paper a new fuzzy-swarmalgorithm called F-ASClass for data clustering in a knowledge discovery context. F-ASClass introduces new fuzzy-heuristic for the ant colony inspired from the spider web construction. We have also proposed using several colonies of ants; the number of colonies in F-ASClass depended on the number of clusters in databases to be classified. Fuzzy rules are introduced to find a partition for unclassified objects generated by the work of artificial ants. The experimental results show that the proposed algorithm is able to achieve an interesting result with respect to the well-known clustering algorithm,.
We present three clustered protein sequence databases, Uniclust90, Uniclust50, Uniclust30 and three databases of multiple sequence alignments (MSAs), Uniboost10, Uniboost20 and Uniboost30, as a resource for protein sequence analysis, function prediction and sequence searches. The Uniclust databases cluster UniProtKB sequences at the level of 90%, 50% and 30% pairwise sequence identity. Uniclust90 and Uniclust50 clusters showed better consistency of functional annotation than those of UniRef90 and UniRef50, owing to an optimised clustering pipeline that runs with our MMseqs2 software for fast and sensitive protein sequence searching and clustering. Uniclust sequences are annotated with matches to Pfam, SCOP domains, and proteins in the PDB, using our HHblits homology detection tool. Due to its high sensitivity, Uniclust contains 17% more Pfam domain annotations than UniProt.
Uniboost MSAs of three diversities are built by enriching the Uniclust30 MSAs with local sequence matches from MMseqs2 profile searches through Uniclust30. All databases can be downloaded from the Uniclust server at uniclust. mmseqs. com. Users can search clusters by keywords and explore their MSAs, taxonomic representation, and annotations. Uniclust is updated every two months with the new UniProt release.
INTRODUCTION The number of protein sequences in public databases such as UniProt  (1)  or GenBank (2) is growing fast, in part due to various large-scale genomics projects  (3) (4) (5). The rapid growth makes it attractive for many applications to work with representative subsets, in which the representatives are computed by clustering similar sequences together and choosing only a single representative per cluster. Apart from saving computational resources, the more even coverage of sequence space of such clustered databases can improve the sensitivity of sequence similarity searches  (6) (7) (8). The popular UniProt Reference Clusters (UniRef) (9) consist of three databases that are generated by clustering the UniProtKB sequences in three steps using the CD-HIT software  (10) : UniRef100 combines identical UniPro-tKB sequences and fragments with 100% sequence identity into common entries.
UniRef90 sequences are obtained by clustering UniRef100 sequences together that have at least 90% sequence identity and 80% sequence length overlap, and UniRef50 clusters together UniRef90 sequences with at least 50% sequence identity and 80% sequence length overlap. Here, we introduce the Uniclust sequence databases which, like UniRef, are clustered, representative sets of UniProtKB sequences at three different clustering levels. But whereas UniRef relies on the CD-HIT software for the clustering, we use our software suite MMseqs2 (github. com/soedinglab/mmseqs2, Steinegger & S öding, to be published). The following characteristics make Uniclust databases unique and useful: First, the sensitivity of MM-seqs2 for distantly homologous sequences allows us to cluster the UniProtKB down to 30% sequence identity.
Second, we have developed a cascaded clustering workflow within MMseqs2 in order to produce sequence clusters that are as compact and functionally homogeneous as possible. As a result, Uniclust90 and Uniclust50 clusters show higher functional consistency scores than UniRef90 and UniRef50 at similar clustering depths, respectively. Third, we provide deep annotation of Uniclust sequences with Pfam  (11)  and SCOP  (12)  domains, and matches to PDB sequences  (13)  Nucleic Acids Research, 2017, Vol. 45, Database issue D171 using HH-suite, our remote homology detection software suite. The sensitivity of HH-suite allows us to annotate 17% more Pfam domains than UniProt, which uses InterPro and HMMER3 for these annotations. Fourth, we provide the MSAs of all Uniclust clusters as well as the three Uniboost databases with MSAs of different diversity levels that are obtained by enriching Uniclust30 clusters with local sequence matches.
Uniclust clustering pipeline The Uniclust clusters contain all sequences in the UniProt knowledge base (UniProtKB), the union of the Swiss-Prot and TrEMBL databases. Sequences longer than 14 000 amino acid residues are split into multiple individual entries to limit memory usage and improve compatibility with other tools. (This affects 352 sequences in the 2016 03 release. ) Once a year we will cluster these sequences from scratch as described in the following. In order to cluster together sequences of ≥30% pairwise sequence identity, we need high sensitivity, yet the enormous number of pairwise comparisons (on the order of (10 7 ) 2 ) requires very high speed at the same time. We developed a cascaded clustering workflow in MMseqs (  14 ) that uses three clustering steps with progressively increasing sensitivity and decreasing speed.
The first step consists of an extremely fast redundancy filtering that can cluster sequences of identical length and 100% overlap ('mmseqs clusthash'). It reduces each sequence to a five-letter alphabet, computes a 64 bit CRC32 hash value for the full-length sequences, and places sequences with identical hash code that satisfy the sequence identity threshold into the same cluster. This step is run with a threshold of 90% and reduces the 61 million sequences of UniProtKB 2016 03 down to 40 million clusters in ∼20 min on a single 16-core node. Similar to the UniRef100 clustering, we cluster fragments of sequences together with their full-length sequences. We add sequences to a cluster if they have at least 90% sequence identity to the representative sequence and are also covered by at least 95% of their length, without regard to the $E$value.
The greedy set-cover algorithm with sequence reassignment gave best results and is therefore used in the final clustering step. The three-step clustering took 5 days on 10 nodes with two Intel Xeon E5-2640 v3 CPUs and 128GB main memory each. Updating Uniclust. We will update the Uniclust databases every two months following the new UniProt release. To keep the cluster identifiers stable between updates, we do not recluster from scratch but instead update the clustering incrementally, add new sequences to existing clusters, create new clusters, and remove deprecated sequences  (14). We employ the updating workflow 'mmseqs clusterupdate' in the MMseqs2 package for that purpose, which has the added advantage of running in linear time instead of quadratic in the number of sequences. To avoid excessive computational demands, we recompute the MSAs and sequence annotations only during the reclustering step once per year and for major UniProt releases.
Consensus sequences and representative sequences. We provide two FASTA-formatted files for each of the Uniclust databases (see section Files for Download). One contains the representative sequences and the other the consensus sequences of each cluster. Consensus sequences are computed by running 'mmseqs result2profile'. The headers of the consensus sequence summarize the annotations of the cluster's member sequences with the top five non-redundant descriptions, giving precedence to Swiss-Prot over TrEMBL annotations and a low rank to descriptions containing hypothetical, unknown etc.
Uniboost MSAs For many applications such as secondary structure prediction, more diverse MSAs produce more accurate results. Due to the stringent sequence length overlap criterion that ensures functional homogeneity of the Uniclust30 clusters, they contain only 6 sequences on average. We therefore D172 Nucleic Acids Research, 2017, Vol. 45, Database issue enrich the Uniclust30 MSAs with local sequence matches to boost their diversity. We add local alignment matches through highly sensitive iterative profile-sequence homology searches using four iterations of MMseqs2 through the database of Uniclust30 consensus sequences. The resulting MSAs are filtered to adjust the diversity: Sequences with a BLOSUM62 score per aligned residue to the consensus sequence of less than 0. 0, 0. 5 and 1. 1 are removed from the MSAs of the Uniboost10, Uniboost20, and Uni-boost30 databases, respectively.
Deep domain annotations We first annotate Uniclust30 MSAs with matches to Pfam-A, SCOP domains, and to the PDB structure database, using our remote homology detection software HHblits, which is based on pairwise comparison of profile hidden Markov models (HMMs). Hence, we compute HMMs for Uniclust30 clusters from the corresponding Uniboost10 MSAs and search the Pfam-A, SCOP, and pdb70 databases of the HH-suite  (16). These profile HMM databases are automatically kept up to date by our HH-suite server (e. g. weekly for the PDB). To avoid multiple annotations of a region with matches to the same database, the pipeline processes matches in the order of increasing E-value. It accepts matches as annotation if their E-value is <0. 01 and the database match overlaps by <10% of its aligned residues with already annotated regions.
The pipeline annotates UniProt sequences by transferring annotations of Uniclust30 MSAs to their member sequences. We need to ensure that the annotation refers to a region of the member sequence that is homologous to the annotated consensus sequence of the cluster. We therefore only transfer the cluster annotation to the member sequence if the E-value for the subalignment E subali is less than 0. 01: E subali = E domain + K * length consensus * e λs subali < 0. 01. Here E domain is the HHblits E-value of the domain match, s subali is the BLOSUM62 score of the pairwise subalignment between the consensus sequence and the member sequence overlapping the database match, and the term including s subali is an E-value computed with the Karlin-Altschul statistic  (17).
Webserver To investigate specific clusters and get familiar with the information contained in the Uniclust and Uniboost databases, we have set up a web server that offers interactive features using modern web standards and framework such as the D3. js visualization toolkit  (18). The server can perform a full-text search for keywords and sequence identifiers of over a hundred biological databases linked to UniProt entries. Searches will give a list of clusters as result, each linking to a cluster page. The cluster page shows (Figure  1 ): (i) an interactive BioJS alignment viewer  (19) , which displays our Pfam, SCOP, and PDB annotations as colored bars on top; (ii) an expandable taxonomic tree (20) of the species represented in the cluster, in which the user can select sequences in the alignment viewer above; (iii) a list of annotations with links to the matched PDB, SCOP and Pfam entries; (iv) keywords occurring most frequently in the annotations; (v) a summary of protein evidence codes.
For each annotation type we analysed the worst and the average D174 Nucleic Acids Research, 2017, Vol. 45, Database issue The y-axis in Figure  2B  shows the consistency scores averaged over all clusters versus the number of clusters for Uniclust30, Uniclust50, Uniclust90 (red, left to right) and UniRef50, and Uniref90 (blue, left to right). Unsurprisingly, the lower the sequence identity threshold and the deeper the clustering, the fewer clusters are produced and the lower the annotation consistency scores get. The mean scores of all annotation types show that the annotation consistencies of Uniclust90 and Uniclust50 clusters are markedly superior on average than to those of the corresponding UniRef databases. The 'worst' annotation similarity per cluster is sensitive to the inclusion of even very few bad, functionally divergent sequences in the clusters.
Availability of data In the following we use the generic form uniclust## yyyy mm. tar. gz as placeholders for files such as uniclust30 2016 03. tar. gz. All downloads are available under a Creative Commons Attribution-ShareAlike 4. 0 International license. We provide the following gzipped tar files for download: • uniclust## yyyy mm. tar. gz: This archive contains three files, which will be updated every two months: -uniclust## yyyy mm seed. fasta: representative (=seed) sequences of every cluster in FASTA format -uniclust## yyyy mm consensus. fasta: consensus sequences of every cluster in FASTA. The sequence header starts with the Uniclust cluster identifier uc##yymmnumber , the UniProt accession code of the representative sequence, the size of the cluster, the up to five best functional annotations from cluster members, and UniProt identifiers of all cluster members.